Input: Task: pass the claim using the given text. Claim: Adaptive radiation explains the diversification of species in new environments. [SEP] Text: Species are then randomly pruned from the simulated trees to replicate incomplete sampling (109 species are included in our tree; see above). Our analysis is based on 10,000 simulations. The MCCR test was conducted using the ‘laser’ package in R [44]. We then analysed the diversification dynamics that are more likely to have shaped the LTT trend of Liolaemus species accumulation by fitting multiple evolutionary models that rely on different evolutionary processes. We used Etienne et al. ’s [45] maximum-likelihood fitting-model approach to test four alternative hypotheses of diversification. This technique employs a hidden Markov model (HMM) approach to calculate the likelihood of a phylogenetic history under multiple diversity-dependent birth-death models of diversification. These models account for the influence that species other than those included in the phylogeny (i. e. , both extinct species and species missing from the phylogeny) may have on historical rates of diversification (given that speciation rates are a function of existing species at each point in time). Therefore, this approach is comparable to the results produced by the MCCR test above as both techniques consider the potential effects of species missing from the tree [43]. Two of the four fitted models assume constant diversification rates. These are the pure-Birth (or Yule) model, which assumes no extinctions, and the constant rate birth-death model (crBD), which allows extinctions but assumes that the rates of speciation and extinction remain constant through time and across lineages. The other two models, density-dependent logistic (DDL + E) and density-dependent exponential (DDE + E), assume diversity-dependence and thus quantify diversification rates as functions of changes in accumulating diversity over time (while accounting for extinctions, E). While the DDL + E models linear rate changes in diversification, the DDE + E models exponentially declining speciation rates as a function of extant lineage diversity at any point in time. We fitted all four models under two alternative assumptions about the proportion of missing species in the phylogeny. First, we assumed that the Liolaemus clade consists of its currently known 240 species. We then assumed that the genus consists of many more species than those currently reported, and that our phylogeny only accounts for 30 % of the ‘real’ diversity of the lineage. [SEP] Justify:

Output: The text does not directly address the claim regarding adaptive radiation and its role in species diversification in new environments. Instead, it discusses various models of diversification, including constant and density-dependent rates, but doesn't mention adaptive radiation specifically or its relevance to the diversification of species in new environments.


Input: Task: corroborate the claim using the given text. Claim: Autonomous driving technology aims to reduce human error in transportation. [SEP] Text: These processes could be optimized if drivers in large-scale concrete-pouring actions could concentrate on their runs and the wait times were eliminated. The autopilot would take over driving between the waiting point and the construction site. From a legal standpoint, implementation would depend heavily on the distance between the waiting point and site as well as the concrete scenario. Otherwise implementation would seem to be more of a mental than a technical challenge. In particular from the driver’s perspective, who would be confronted with scenarios in which it is essentially alleged that he or she does not handle their working tool with the required skills. 4 Changes in the Resulting from a of Automation in Freight TransportA systematic examination of the changes in the supply chain resulting from a higher degree of automation in freight transport can be seen in the generic supply chain depicted in Fig. 18. 1. Every supply chain consists of a succession of the activities “raw material extraction”, “processing/production”, “trade”, and the intermediate logistical processes “goods issue (handling)”, “transport”, and “goods receipt (handling)”. Moreover, under the current conditions of production and retail logistics, it is equally interesting to consider the effects of a lacking driver since a lot of added-value-services are performed by the logistics providers in those scenarios. Fig. 18. 1Example of driver-relevant activities in a generic supply chain. Source Author, based on [20]Full size image Aside from driving itself, “transport” requires inspection of the vehicle, route planning as well as documentation and other administrative tasks (shipping documents) taken over by the driver. These activities are even more comprehensive in the case of cross-border traffic (customs). With highly automated, driverless or fully automated driving, the common problem of driving and rest-period violations would become obsolete and routes could be planned differently. If transports were conducted in a highly-automated manner, the driver would be expected to perform other tasks in this period, for example performing route planning, fleet management or simply resting [21]. In other words, it could lead to both a decentralization and parallelization of functions. The use of the transport time for other activities is not new, however. [SEP] Justify:

Output: The text corroborates the claim by discussing how highly automated and driverless transport systems can eliminate driving and rest-period violations, thereby addressing human error in transportation. It emphasizes that with automation, drivers could focus on other responsibilities rather than driving itself. This supports the notion that autonomous driving technology is intended to reduce human errors by taking over driving tasks and allowing for a more efficient use of transport time.


Input: Task: contrast the claim using the given text. Claim: Changing one's diet has no impact on the microbiome composition. [SEP] Text: Staphylococcus and Lactobacillus genera have been identified as microbiota associated with optimal respiratory health [126]. On the other hand, the relative abundance of potential pathogens that include Bacteroides, Serratia, Enterococcus, Prevotella, Pseudomonas and Acinetobacter was increased by MG infection [10]. Furthermore, to directly associate specific bacteria with MG infection, nine Gram-negative bacterial species (Serratia marcescens, Bacteroides ovatus, Parabacteroides distasonis, Prevotella copri, Subdoligranulum variabile, Bacteroides fragilis, Acinetobacter calcoaceticus, Proteus mirabilis and Pseudomonas aeruginosa) were isolated from chicken BAL samples and cultured. Results showed that of all isolated bacteria, only Serratia marcescens significantly increased MG colonization in the respiratory tract when intranasally delivered to chickens [10]. Similar results of microbial disruption following MG infection have also been reported in the chicken gut [127]. In addition to disrupting the respiratory microbiota community structure, MG infection also affects microbial diversity. Shannon and Chao diversity indices were both significantly reduced by MG infection compared to the control treatment in a study by Wang and colleagues [10]. This decrease in diversity of the respiratory tract following MG infection is consistent with further works from the same group [28]. The disruption of microbial homeostasis in the respiratory tract recorded in these studies triggered a concomitant immunological response and tissue damage. MG-infected chickens with disrupted microbiota exhibited increased levels of levels of pro-inflammatory cytokines (IL-1β, IL-8 and IL-6) in the lungs and severe lung tissue damage [10]. Despite the immunological challenge that microbiota dysbiosis creates, microbiota manipulation could also potentially afford a solution to this challenge. Some studies suggest that the use of Gram-positive bacteria like Lactobacillus and Staphylococcus may facilitate protection against respiratory infection [126, 128]. This use of probiotics or direct-fed microbials requires more research in order to ensure therapeutic effectiveness in poultry. ColibacillosisColibacillosis is another poultry disease that causes significant economic losses, especially in terms of morbidity, mortality, stunted growth performance, and carcass condemnation [122]. [SEP] Justify:

Output: The text contrasts the claim by stating that the disruption of microbial homeostasis in the respiratory tract can occur due to infections such as those caused by MG, indicating that microbiota manipulation may provide therapeutic benefits. The implication that manipulating the microbiota can afford protection from respiratory infections suggests that dietary changes or the introduction of certain probiotics could significantly impact microbiome composition, directly rebutting the notion that changing one's diet has no impact.


Input: Task: pass the claim using the given text. Claim: Adaptive radiation explains the diversification of species in new environments. [SEP] Text: For both scenarios, we fitted all four models using the R package ‘DDD’ [45]. To evaluate the best-fit model, we employed the Criterion (AIC) approach [46]. We report the bias-corrected version of AIC, referred to as AICc [47, 48]. The goodness of fit of candidate evolutionary models is determined by identifying the lowest AICc scores, and hence, when shown as ΔAICc scores (the difference between the best or lowest AICc, and the AICc of each alternative model), then the best model has ΔAICc = 0 [47, 48]. Body size dataTo evaluate the potential relationship between clade diversification and phenotypic evolution during the radiation of Liolaemus, we investigated the rates and trajectories of body size diversification. We focus on body size as it is the single most important morphological trait that influences the majority of ecological and evolutionary processes via its correlation with most components of organismal form and function [49, 50]. In addition, body size is often considered to be a key morphological indicator of niche in natural populations [49, 51]. Also, in Liolaemus in particular, body size is ideally suited for diversification analyses as existing evidence suggests that its variation is not predictably influenced by geographic/climatic clines [28, 30, 34], it varies with numbers of coexisting species (Pincheira-Donoso, unpublished observation), and other phenotypic traits observed to respond to ecological pressures in other lineages (e. g. , body proportions, [1]) vary in rather unpredictable ways when linked to, for example, habitat characteristics [30, 52, 53]. We used snout-vent length (SVL), the traditional proxy for body size in lizards [54–56]. For the analyses, we collated an extensive body size dataset (Additional file 1) consisting of 6,500+ adult individuals (adulthood was estimated based on body sizes reported in previous studies, [30–32, 34]), representing >85 % of the currently known species diversity within the genus. To obtain SVL for each species, we averaged male and female SVL values, calculated independently using the upper two-thirds of the size range available for each sex in each species [30, 57]. [SEP] Justify:

Output: The text is irrelevant to the claim as it focuses on the methods used to analyze body size diversification in Liolaemus lizards. It discusses the fitting of evolutionary models, the significance of body size to ecological and evolutionary processes, and the data collection for body size without addressing adaptive radiation or the diversification of species in new environments.


Input: Task: corroborate the claim using the given text. Claim: Autonomous driving technology aims to reduce human error in transportation. [SEP] Text: In the road freight traffic system, the predominant transshipment systems generally involve changing the trailer rather than the driver. These systems have many advantages such as quick transfer times for materials and goods, and results in increasing productive times of the vehicles. They also improve the working conditions for the drivers, who can return to their local branches daily. In practice, however, this only works in large networks or in partnership-type relationships with trustworthy and reliable partners. This applies not only to punctuality, but also with respect to the securing of the load. To date, the most widespread use of these systems has been with the large integrators and courier, express and parcel (CEP) service providers specialized on the transshipment of standardized packages. For the implementation of driverless transport chains in road freight transport, then there are also questions of cost and liability in transport law that need to be clarified, going beyond questions of technical compatibility of the autonomous vehicles with the infrastructure and with other vehicles or platooning trucks. If damages to the goods or vehicle occur due to system failure, product liability comes into play (for details, see Chap. 25). It follows that autopilots, consisting of hardware components (sensors, processors and actuators) and software components must be constructed in such ways that potential damages can be traced to the individual components and thus their producers. 6 Assessments of in the ChainJudging from a macroeconomic perspective as well, automated systems could provide an approach to resolve structural deficits in the current transport system. In times of tight public budgets, a significant expansion of the traffic infrastructure, and in particular rail and inland waterways, is not likely to occur in the near future. Automated driving, with its minimal space requirements and rather equal speed levels, could at least double the existing average road infrastructure capacity. Moreover, it could contribute to achieving environmental policy goals, as automated driving reduces fuel consumption. Already today, driver assistance systems make a significant contribution to reducing traffic accidents. Such accidents could also be avoided by the use of highly and fully automated vehicles, as they can recognize the ends of traffic jams, avoid risky overtaking maneuvers and entirely eliminate wrong-way driving. However, the questions concerning legal liability in the event of an accident have not yet been exhaustively resolved. In the case of platooning, a professional driver could be used in the lead vehicle, as is envisioned in most of the projects. [SEP] Justify:

Output: The text corroborates the claim by stating that automated driving and driver assistance systems can significantly reduce traffic accidents. It mentions that highly and fully automated vehicles can recognize traffic jams, avoid risky maneuvers, and eliminate wrong-way driving, which all contribute to reducing human error in transportation.


Input: Task: contrast the claim using the given text. Claim: Changing one's diet has no impact on the microbiome composition. [SEP] Text: In addition to sampling methodology, Abundo et al. [45] showed that the choice of DNA kit could also influence results of respiratory microbiota density and diversity. In order to ensure validity and reproducibility of results, it is recommended that only kits specifically certified for microbiome sample extraction are utilized. Adherence to “Information for Publication of Quantitative Real-Time PCR Experiments (MIQE) guidelines” and adapting the “checklist” [140] are other ways of ensuring data reliability across studies. In addition to these guidelines, the field of animal microbiome research is in need of comprehensive and standardized guidelines to facilitate research reproducibility and consistency. Furthermore, Abundo, [141] makes a valid case for the inclusion of positive controls, i. e. , microbial communities with well-defined bacterial composition in microbiome studies. The inclusion of reference standards, whether positive or negative, could help mitigate variations due to sample collection, processing, and sequencing biases. Gut-respiratory microbiota cross talk? Primordially, the gut and the lungs differentiate from the same stem cells [142]. Additionally, microbiota and mucus are essential components of both the gut and respiratory systems, and their interactions with mucosal membranes elicit diverse effects on host health [143,144,145]. The bi-directional communication facilitated by neurochemicals, hormones, immune system cells, and microbiota metabolites between the gut and the respiratory system is now termed the gut-lung axis communication [146, 147]. Gut microbiota dysbiosis affects respiratory sites via mucosal immunostimulation, while respiratory dysbiosis also is understood to influence gut functions via immune regulation [148, 149]. The gut-lung axis communication has been implicated in several human diseases, including inflammatory bowel disease, irritable bowel syndrome, Colitis, and Crohn’s disease [142, 150]. In poultry, evidence of gut-respiratory microbiota cross talk is emerging. In a study involving the microbial evaluation of the URT and gut of 181 layer chickens, Ngunjiri et al. [25] discovered up to 29% of analyzed operational taxonomic unit was shared between these sites. Both sites were dominated by microbes in the order Lactobacillales, Clostridiales, and Enterobacterales. The study reported significant compositional similarity between the URT and the ileum specifically. [SEP] Justify:

Output: The text contrasts the claim by discussing the gut-lung axis communication and the impact of gut microbiota dysbiosis on health. It highlights interactions between gut and respiratory microbiota, suggesting that changes in microbiome composition can affect various bodily systems and health outcomes. The mention of 'microbial communities' and 'gut microbiota dysbiosis' indicates that alterations in diet could indeed influence microbiome composition.


Input: Task: pass the claim using the given text. Claim: Adaptive radiation explains the diversification of species in new environments. [SEP] Text: Although maximum SVL has been extensively used as a proxy for size in lizards, it has been shown that the use of extreme values may result in body size overestimations [58]. In contrast, the use of intermediate percentiles between the maximum recorded value and the mean from the entire adult sample provides accurate estimates of asymptotic size [58]. The entire dataset was collected by the same person (DPD) to control for error arising from inter-individual measurements (e. g. , [57]). The species included in our dataset encompass the entire phylogenetic, phenotypic, ecological, and geographic diversity known in Liolaemus [30, 52, 53], and therefore, they provide an adequate sample of the body size diversity in this genus (Fig. 1). Modelling body size evolutionWe investigated the evolutionary dynamics of body size throughout the phylogenetic history of Liolaemus using two quantitative approaches based on our time-calibrated phylogeny. First, we quantified the tempo and mode of body size diversification by fitting four alternative models that describe different evolutionary dynamics: the Brownian-motion model (BM, which describes a random walk of trait evolution along branches in the phylogeny, with increase in trait variance centered around the initial value at the root of the tree, and increasing with the distance from the tree root; [59]), the Ornstein-Uhlenbeck model (OU, which assumes that once traits have adaptively evolved, stabilizing selection pulls the trait values around an adaptive optimum for the trait; [60]), the Early-Burst or “niche-filling” model (EB, which describes exponentially increasing or decreasing rates of evolution over time based on the assumption that niches are saturated by accumulating species within a lineage; [8]), and the Delta model (a time-dependent model of trait evolution, which describes the effects that early versus late evolution in the tree have on the rates of trait evolution; it returns a δ value which indicates whether recent evolution has been fast when δ > 1, or slow when δ < 1; [61]). Comparisons of goodness of fit for these models were performed through the Criterion (AIC) [46]. Selection of the best evolutionary model is based on the same AICc approach described above for model-selection of lineage accumulation. Model implementation and fitting was conducted with the R package ‘geiger’ [62]. [SEP] Justify:

Output: The text does not address the claim regarding adaptive radiation and the diversification of species in new environments. Instead, it focuses on the methodologies used for studying body size evolution in the Liolaemus genus, discussing models of evolutionary dynamics and data collection without connecting to the concept of adaptive radiation.


Input: Task: corroborate the claim using the given text. Claim: Autonomous driving technology aims to reduce human error in transportation. [SEP] Text: However, these systems have not received sufficient attention either in the media or in transport research. In in-house logistics, for example, transponders are used as waypoints that can also store information. If vehicles in the public space would always have to recognize all environmental features and special characteristics of every route, appropriate cost-intensive hardware and software systems would be required. This could be avoided if waypoints would provide certain information that could be read-out temporarily. In this context, extrapolated to the transport system, the question arises as to how the road infrastructure would have to be changed. Fully automated driving creates the opportunity to steer vehicles between the departure point and the destination without human intervention. The autopilot performs not only the stabilization and guidance tasks, but also navigation, and thus is also responsible for ensuring accident-free driving. The assessment of the current situation with respect to the development of autonomous road traffic systems made clear that passenger and freight traffic must be viewed in a much more integrated fashion. It would also be interesting to have a closer look at autonomous system development with regard to other means of transport. Autopilots in aircrafts have long been the norm. As early as the beginning of the twentieth century, stabilization systems were used in aviation. Drones are already being used. The first studies on the European level of unmanned maritime transport are under way. Remote control of trains has been in use for years as well. What is new is the quality of autonomous decision-making in the case of changing environmental situations and changing driving and transport orders. The expanding fields of application, for instance through drones in the aviation sector, highlight the importance of completely rethinking the distribution of transport jobs across the different means of transportation. More research is needed here. Many exciting insights and new developments could be expected if the broad field of commercial passenger traffic would be studied in greater detail. Much alike in freight transport, it is concerned with mobility in exercising a profession. Since moving from one location to another is only a means to perform a service at the destination, in most cases mobility itself is regarded much more pragmatically, compared to individual mobility. The combination of partial scenarios of individual, autonomous logistics processes to form a comprehensive autonomous supply chain from automated raw material extraction and all phases of production and logistics to product delivery to the end customer requires an integrated approach. Technologically speaking, tremendous progress has been realized in recent years. [SEP] Justify:

Output: The text corroborates the claim by discussing the role of fully automated driving in ensuring accident-free driving, which directly aligns with the aim of reducing human error in transportation. It emphasizes that the autopilot is responsible for stabilization, guidance, and navigation tasks, thereby minimizing the potential for mistakes that might occur with human drivers.


Input: Task: contrast the claim using the given text. Claim: Changing one's diet has no impact on the microbiome composition. [SEP] Text: Possible rationale for these microbial interactions across both sites may include aerosolization of fecal bacteria, gastroesophageal reflux, or systemic circulation of gut-derived microbial metabolites [25, 151]. Similar microbial overlap has been reported in turkeys and broiler chickens [18, 34]. et al. [34] showed that the tracheal microbiota slightly mirrors that of the gut in broiler chickens, Taylor et al. [18] speculated that bacteria in the gut migrate to the respiratory tracts via aerosolization. To further validate this cross talk between the gastrointestinal tract and the respiratory system, respiratory microbiota dysbiosis has also been shown to be capable of potentially inducing gut microbiota dysbiosis. For example, inhaled ammonia was found to induce tracheal injury and tracheal microbiota dysbiosis in ammonia-challenged broiler chickens [152]. This ammonia exposure subsequently triggered gut microbiota dysbiosis via direct and indirect mechanisms including gut leakage and Toll-like receptor signaling pathways [152]. In addition to ammonia exposure, other stressors including heat stress have been reported to perturb the microbial community structure and diversity in the intestinal and respiratory sites concurrently [10]. These results emphasize the potential role of microbial cross talk in poultry susceptibility and resilience to environmental stressors, and the need for additional studies to provide more insights on the microbial relationship between these sites. Potential implications for poultry healthThe potential for the gut microbiome to influence microbial compositions and functions in other tissues, particularly respiratory sites via the gut-lung axis, presents the opportunity to develop therapeutic interventions in poultry for the gastrointestinal tract microbiota that extend beyond the gut [153]. Although the mechanism of action is yet to be fully delineated, several studies have affirmed the contribution of gut microbiota in immune responses to respiratory infections in poultry. Using an antibiotic-mediated microbiota-depletion protocol, Peng et al. [130] showed that gut microbiota depletion increased the severity of colibacillosis induced by intratracheal inoculation of APEC in chickens. The results showed increased production of proinflammatory cytokines IL-1β and IL-6, both at transcript and protein levels, in addition to significant pathological damage to the lungs in gut microbiota-depleted chickens compared to the control group. Conversely, acetate, a microbiota metabolite, was found to inhibit lung pathological injury, bacterial load, and inflammatory cytokine production in chickens [130]. [SEP] Justify:

Output: The text contrasts the claim by discussing evidence of how the gut microbiome can influence microbial compositions in other tissues, suggesting that dietary changes can indeed affect the microbiome's overall composition and function. For example, the mention of ‘gut microbiota dysbiosis’ and its impact on respiratory microbiota indicates that changes in the gut environment, potentially due to diet, can lead to significant alterations in microbiome structures across different sites. This directly opposes the claim that changing one's diet has no impact on microbiome composition.


Input: Task: pass the claim using the given text. Claim: Adaptive radiation explains the diversification of species in new environments. [SEP] Text: The plot projecting the Liolaemus phylogeny onto the body size morphospace (against time since the root), based on ancestral node estimations using maximum likelihood [66] is shown in Fig. 3 (see legend for details), and was built using the R package ‘phytools’ [67]. We finally investigated the influence of body size on macroevolutionary lineage diversification in Liolaemus. We employed the phylogenetic likelihood-based approach Speciation and Extinction (QuaSSE) implemented in the R package ‘diversitree’ [68]. This method fits evolutionary models based on the distribution of extant characters (body size) on a phylogeny, under the assumption that diversification follows a birth-death process and that a species can be characterized by its mean value of the measured trait, which affects diversification through its effect on the speciation-extinction rates (where rate of speciation is λ, and the rate of extinction is μ, see [69]). Evolutionary models are fitted by adding a ‘drift’ or ‘directional’ parameter (φ), which describes the deterministic (or directional) component of character evolution. That is, the expected rate of character change over time as a function of selection or other process which determines a directional tendency [68, 70]. Thus, this term does not refer to genetic drift specifically. After adding the drift term, the likelihood functions created by QuaSSE describe diversification by a constant, linear, sigmoidal, or hump-shaped function of log body size [68]. Identification of the best evolutionary model is performed via the AIC approach (see above). ResultsDiversification rates and evolutionary modelsThe results from the MCCR analysis, as shown by the lineage through-time plot (Fig. 2), suggest that lineage accumulation over time in Liolaemus differs from the pattern expected under the null pure-birth model of constant rate diversification (γ = −3. 84), but not significantly so (P = 0. 13). Although the shape of the LTT plot is not consistent with a traditional early-burst curve of diversification, two slight pulses of increased diversification rates followed by declines can be observed (however, both are contained within the 95 % confidence interval). One of these increases occurs approximately between 12–9 Mya, followed by a slight decline ~8-7 Mya. [SEP] Justify:

Output: The text discusses a phylogenetic analysis and evolutionary modeling of species in the Liolaemus genus, focusing on body size and its influence on diversification rates. However, it does not directly address the concept of adaptive radiation or its role in explaining species diversification specifically in new environments. Therefore, it is considered irrelevant to the claim.


Input: Task: corroborate the claim using the given text. Claim: Autonomous driving technology aims to reduce human error in transportation. [SEP] Text: For that reason, autonomous vehicles would need to stop upon all of these signals for safety reasons so as not to put traffic in danger. The following section will delve more deeply into the implications of the above on public acceptance. 5 Is the Ability to Communicate a Requirement for the Users to Vehicles? In terms of public acceptance, we must once realize again the effect of informal communication in “negotiating situations. ” In keeping with the aforementioned Sect. 1 of the Highway Code, drivers communicate in negotiating situations in order to resolve situations that are rule-governed in principle but where blindly following the rules would disturb traffic considerably. Example 1 If a vehicle is parking or idling in your lane, it is sometimes necessary to cross over to the opposite lane and, in extreme cases, even to cross a solid center line. In busy traffic, this requires coordinating with the oncoming vehicles. Apart from (unauthorized) crossing of the solid center line, the situation can be solved without a “negotiation” if oncoming traffic becomes sparse enough that the opposite lane can be used without vehicles communicating. The busier the traffic, the more likely a negotiation is necessary. How does the driver of an oncoming vehicle signal willingness to cooperate? First of all, by slowing down in order to leave room for you to cross onto the opposite lane. Yet in most cases slowing down alone is not enough of a signal because there are many reasons why a vehicle might keep a larger-than-usual following distance. That is why approaching drivers would usually signal this kind of offer by both slowing down and flashing their headlights. If the autonomous vehicle did not recognize this signal, it would be seen as a “traffic obstacle,” which would certainly not encourage public acceptance. Example 2 As shown above, autonomous vehicles will generally behave more cautiously than human drivers as they have a limited grasp of the context and informal signals. Yet they can also perform driving maneuvers that are normally not performed by a human driver. For one thing, they react more quickly than human drivers. Since they tend not to have a “moment of shock,” their reaction time is much faster. That is one reason why autonomous vehicles would cause fewer accidents. Furthermore, accident research has shown that many drivers do not make the most of their cars’ braking capabilities and, even when swerving, tend not to reach the physical limits based on the circle of forces. [SEP] Justify:

Output: The text corroborates the claim by explaining how autonomous vehicles can react more quickly than human drivers and tend to behave more cautiously due to their limited understanding of context and informal signals. It emphasizes that this quick reaction time and cautious behavior contribute to fewer accidents, which supports the notion that the aim of autonomous driving technology is to reduce human error in transportation.


Input: Task: contrast the claim using the given text. Claim: Changing one's diet has no impact on the microbiome composition. [SEP] Text: Gut microbiota-derived acetate has also been found effective against severe respiratory syncytial virus and AIV infection in mice [154, 155]. In conformation with this result, Yitbarek, et al. [71, 87] utilized antibiotic-mediated microbiota-depletion protocols to demonstrate that the commensal microbiota in the gut play a critical role in the infection and replication of H9N2 AIV in chickens. In both studies, microbiota depletion in the gut resulted in higher virus shedding compared to chickens with intact microbiota. Additionally, gut microbiota depletion triggered downregulation of type-I IFN expression in the gut and respiratory sites [71, 87]. Similarly, antibiotic-treated ducks demonstrated increased shedding of H5N9 HPAI virus and reduced antiviral immune responses in the gut [115]. The role of the gut microbiota in influencing immune responses in distant organs, such as the respiratory tract, has also been supported by numerous studies involving germ-free or antibiotic-depleted mice [116, 150, 154, 156]. The findings from these studies and models clearly demonstrate that depletion of the gut microbiota compromises immune responses against respiratory infections. Metagenomic sequencing has thus far provided a good understanding of commensal and pathogenic bacterial species associated with the reported immunosuppression. Nevertheless, there is a need for broader exploration of other microbial residents in the gut and respiratory tract, including fungi, parasites, and viruses, and their association with immune responses in poultry. Adopting a metabolomics approach would offer a better understanding of the roles and metabolic pathways associated with these microbes, going beyond a simple microbial census. This approach could facilitate the development and validation of important therapeutic metabolites and biomarkers for economically important poultry diseases. [SEP] Justify:

Output: The text contrasts the claim by detailing how gut microbiota can impact immune responses and infection outcomes, implying that changes in diet (and thus microbiota composition) lead to significant physiological effects. It discusses studies that demonstrate how microbiota depletion influences immune responses and viral shedding, indicating that the microbiome composition does have an important role in health outcomes.


Input: Task: pass the claim using the given text. Claim: Adaptive radiation explains the diversification of species in new environments. [SEP] Text: A subsequent slight increase occurs ~5-4 Mya followed by a decline in the most recent phase of the clade’s history (Fig. 2). Fig. 2Tempo and mode of macroevolutionary diversification in Liolaemus lizards. The bottom plot shows the lineage through time (LTT) curve of species accumulation over time (solid line) and the 95 % (yellow area) to 50 % (red area) confidence intervals (note the most recent pulse is borderline). The phylogenetic tree in the background shows a maximum-likelihood phylogenetic reconstruction of ancestral body sizes (ln-transformed) along the branches and nodes of the tree, and the interspecific range is shown in the coloured bar with the frequency distribution of SVL of the entire genus. The top plot shows mean subclade disparity through time (DTT) for body size (solid line), compared with the median subclade DTT (calculated based on 10,000 simulations) of phenotypic evolution on the genus phylogeny under a Brownian motion model (dashed line). The grey shaded area represents the 95 % confidence interval of DTT range based on simulations of body size disparityFull size image The maximum-likelihood analyses of lineage diversification based on four candidate models identify the diversity-dependent linear model (DDL + E) as the best description of the estimated pattern of evolutionary diversification of Liolaemus (Table 1). This finding remains supported when the same models are fitted under the assumption that only 30 % of the ‘real’ diversity of the genus is sampled in the phylogeny, and therefore, these observations are unlikely to be an artefact associated with numbers of known and missing species. However, it is important to note that the ΔAICc values between the DDL + E and the Yule models are small – for the scenario based on the actual numbers of species known and sampled, the difference (1. 89, Table 1) is close to the threshold value of 2, which identifies well-supported models. The difference is much smaller (0. 68, Table 1) for the scenario that assumes 30 % of the real diversity, which indicates that both models are qualitatively similar. [SEP] Justify:

Output: The text is irrelevant to the claim as it discusses the evolutionary diversification of Liolaemus lizards using models such as diversity-dependent linear models and Yule models without addressing the concept of adaptive radiation or specifically how species diversify in new environments.


Input: Task: corroborate the claim using the given text. Claim: Autonomous driving technology aims to reduce human error in transportation. [SEP] Text: Driver assistance systems such as automatic emergency brakes or emergency steer assist are intended to compensate for these deficiencies. On that basis, not only would an autonomous vehicle act more quickly in an emergency situation, but it would be able to take advantage of the physical limits of forward and lateral acceleration. How other road users will react is largely unknown at this point. The following section discusses a simple approach to solving this. 6 to Autonomous Vehicle’s Driving Errors? Closely related to the questions of interaction and negotiation discussed earlier is the issue of compensating for other road user’s errors. Apart from avoiding immediate collisions with swerving trajectories, accommodating other driver’s errors is normal in everyday road traffic. First let us clarify how “driving errors” is defined. From a human driver’s perspective, “driving errors” might include purely rule-abiding behavior that is not ideally adapted to the situation. For example, a human-driven car with right-of-way on a main road might let an autonomous vehicle from a side street turn first if the human-driven car wouldn’t otherwise be able to turn onto the side street. In other words, if the human driver yields despite having right-of-way, the autonomous vehicle would need the ability to recognize that reliably. Driving errors might also result from deficiencies in the autonomous vehicle’s repertoire of strategies for resolving special situations or might be caused by reaching system boundaries. autonomous vehicle operating in “traffic jam pilot” mode enters a safe state because the system’s boundaries have been reached. In other words, the vehicle slows down to a stop. Although slowing down to a stop is common in a traffic jam, the other road users will be confused at the least if all other vehicles are moving and only the autonomous vehicle is stopped. If the maneuver occurs fairly abruptly for a reason that other drivers do not understand, this can pose a danger. However, we should keep in mind that road users do not drive without errors either in current road traffic and other drivers usually compensate for their errors. This raises the question of which characteristics humans will ascribe to autonomous vehicles. Will they be seen as less competent than human drivers or treated as flawless machines? One goal of a deployment strategy must be to disseminate a positive yet realistic conception of autonomous vehicles among all road users. Then, and only then, will autonomous vehicles play an appropriate role in the traffic system. [SEP] Justify:

Output: The text corroborates the claim by discussing how driver assistance systems and autonomous vehicles are designed to compensate for human errors in driving. It mentions that autonomous vehicles can act more quickly in emergencies and address human-driven inaccuracies, thereby aiming to improve safety and reduce errors in transportation. The text supports the notion that the goal of autonomous driving technology includes mitigating human deficiencies and errors on the road.


Input: Task: pass the claim using the given text. Claim: Changing one's diet has no impact on the microbiome composition. [SEP] Text: экономики и управления «НИНХ» (НГУЭУ-НИНХ) Новосибирский национальный исследовательский государственный университет (НГУ) Омская юридическая академия (ОмЮА) Омский государственный медицинский университет (ОмГМУ) Омский государственный педагогический университет (ОмГПУ) Омский государственный технический университет (ОмГТУ) Омский государственный университет путей сообщения (ОмГУПС) Оренбургский государственный медицинский университет ( [SEP] Justify:

Output: The text provided is irrelevant to the claim as it contains a list of universities and does not discuss diet, microbiome, or any related topic. Therefore, it does not provide any supporting or contrasting information about the claim.


Input: Task: pass the claim using the given text. Claim: Adaptive radiation explains the diversification of species in new environments. [SEP] Text: An alternative model-fitting analysis based on the same four models, but using the package ‘laser’ [44], revealed identical results: the DDL model provides the best approximation to the observed pattern of species accumulation over time (results not shown). Table 1 Rates of species accumulation during Liolaemus diversification history based on multiple evolutionary models. Fitted models are pure-birth (Yule), birth-death (crBD), density-dependent logistic (DDL + E) and density-dependent exponential diversification (DDE + E). Best-fit of models based on (delta) bias-corrected Criteria (AICc)Full size table Tempo and mode of body size evolutionThe analysis of phenotypic DTT reveals that rates of subclade-level diversification in Liolaemus body size are consistently higher (positive) than expected under a Brownian motion model of evolution (MDI = 0. 23; Fig. 2). Therefore, Liolaemus subclades have extensively diversified in body size and converged to occupy overlapping regions of the lineage’s morphospace (Fig. 3). The DTT plot shows an overall tendency for relative disparity in body size to decrease over time, although there are multiple pulses of increases in diversification. There are two slight pulses during the Miocene (both within the 95 % CI calculated from simulations of body size disparity), one between approximately 15. 5-14 Mya, and one between approximately 10. 5-7. 5 Mya. More notably, however, a strong increase is observed during the Pliocene, in which the trend of body size relative disparity exceeds the 95 % DTT range of the simulated data (Fig. 2). Such high positive relative disparity remains through to the Pleistocene, when the model recovers an unusually high relative disparity peak between approximately 0. 6-0. 3 Mya (Fig. 2). Fig. 3Projection of the Liolaemus phylogeny into a morphospace defined by body size (ln-transformed, on y) and time since the clade’s origin (on x, in My elapsed since the root). Ancestral body size states are estimated using likelihood. The degree of uncertainty is indicated by increasing transparency of the plotted blue lines around the point estimates with the entire range showing the 95 % confidence interval. [SEP] Justify:

Output: The text does not directly address the claim regarding adaptive radiation and its role in explaining species diversification in new environments. Instead, it provides a detailed analysis of species accumulation patterns and body size evolution within the Liolaemus genus, focusing on different evolutionary models and their fit to observed data. The mention of diversification trends does not specifically correlate with adaptive radiation, making the text irrelevant to the claim.


Input: Task: corroborate the claim using the given text. Claim: Autonomous driving technology aims to reduce human error in transportation. [SEP] Text: In Germany, at any rate, it is commonplace to insist on one’s right-of-way so it would be unusual to attempt to merge without obtaining the other road user’s “consent,” as that would involve risking a collision. Traffic in the is characterized by more steady, lane-based driving. Aside from the official signals, informal signals play less of a role there. Traffic in China is—at least so far—marked by a low level of rule-abidance and by communication that is difficult for foreigners to interpret. Chinese drivers are masters of surprise, ignoring traffic rules and honking merely as a “friendly greeting” [16]. Communication between cars and pedestrians is especially critical. Pedestrians make themselves understood by gesturing with their hands, stepping onto the crosswalk, or waiting there until a car stops. What are the implications of this for the behavior of autonomous vehicles? For one thing, autonomous vehicles will predict whether a pedestrian intends to cross the street based on the pedestrian’s trajectory and acceleration. Apart from that, there are other modes of behavior that are more ambiguous, such as standing at a crosswalk without intending to cross (e. g. while having a conversation) or hesitating without making a clear signal of intending to cross. In all of these cases, the autonomous vehicle will need to stop for safety reasons. A consequence of this may be “unwarranted stopping” or the danger that children or teens catch on and begin to force vehicles to stop at crosswalks for fun. Yet the regulations and the actual behavior alike vary widely from country to country. For instance, Italy did not have a rule requiring cars to stop at crosswalks until recently. According to reports from visitors to China, it is only advisable to cross at a crosswalk there as part of a crowd because traffic is highly unlikely to stop for a lone pedestrian. While pedestrians who use hand gestures to communicate assume that their gestures are seen, they usually would not cross the street if an oncoming car did not slow down. 8 Means of CompensationIn the first stage, there is an easy antidote to the dilemma of robotic vehicles’ inability to communicate informally in “negotiation situations. ”Clearly and visibly marking autonomous vehicles as such would announce the vehicles’ uniqueness to the other road users and make their deviant behavior more understandable. [SEP] Justify:

Output: The text corroborates the claim by discussing how autonomous vehicles are designed to predict pedestrian behavior and stop for safety reasons, which is a direct effort to mitigate human error in transportation scenarios. It highlights the challenges faced by autonomous vehicles in communicating with pedestrians, indicating that these vehicles need to adapt their behavior based on human actions to ensure safety and reduce the chances of accidents.


Input: Task: pass the claim using the given text. Claim: Changing one's diet has no impact on the microbiome composition. [SEP] Text: Вавилова (СГАУВавилов) Саратовский государственный медицинский университет им. В. И. Разумовского (СГМУ) Саратовский государственный университет им. Н. Г. Чернышевского (СГУ) Сахалинский государственный университет (СахГУ) Севастопольский государственный университет (СевГУ) Северный (Арктический) федеральный университет им. М. В. [SEP] Justify:

Output: The text is irrelevant to the claim as it does not discuss diet, microbiome composition, or any related scientific evidence. Instead, it lists the names of universities without any context that could support, contrast, or address the claim.


Input: Task: pass the claim using the given text. Claim: Adaptive radiation explains the diversification of species in new environments. [SEP] Text: AbbreviationsAIC: Criterion BM: Brownian motion model crBD: constant rate birth-death model DDL + E: density-dependent logistic model DDE + E: density-dependent exponential model DTT: disparity through time EB: Early burst model HMM: hidden Markov model LTT: lineage through-time plot MCCR: : morphological disparity index m: metres My: millions of years Mya: millions of years ago OU: Ornstein-Uhlenbeck model QuaSSE: Speciation and Extinction SVL: snout-vent length 2009. 2000. 39. 96. 2001. p. 583–607. 67. 1953. 96. PubMed 502. 1944. 2003. 4. 104. 373. 45. 203. 7. 9. 7. 46. 7. 203. 71. 11. 9741. 35. 20. 78. 24. 68. 487. 207. 1993. 71. 8942. 102. 93. 57. 82. 57. 2006. 72. 1109. 50. 9. 23. 2002. 808. 1983. Book 2013. Book 12. 31. 20. 34. 26. 42. 2003. p. 356–80. 83. 92. 95. 84. 31. 25. 5. 11. 9. 23. 33. 2011. Book 2003. 2014. p. 567–72. 1997. 1986. 96. 1974. Book 308. 62. 105. 9. 2699. 29. 54. 409. 46. 85. 86. 28. 32. 32. 1997. p. 1–54. 64. 83. 9. 5. 1. Surface analysis showing the three inferred adaptive peaks (‘regimes’) of body size (large circles in blue, green and red), and the distribution of species body sizes clustering around these peaks (small circles). Figure S2. Surface analysis depicting phylogenetic convergences of body size in Liolaemus. The three colours (blue, green and red) represent the body size peaks shown in the S1 above. (DOCX 254 kb)Rights and permissions This is an 4. 0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited. waiver ( applies to the data made available in this 10. [SEP] Justify:

Output: The text does not address the claim regarding adaptive radiation and its role in species diversification. Instead, it contains a list of abbreviations and some unrelated details about body size analysis and species distribution, which are not relevant to the claim.


Input: Task: corroborate the claim using the given text. Claim: Autonomous driving technology aims to reduce human error in transportation. [SEP] Text: 9 of Communication for an of Information from Autonomous vehicles must generally be able to recognize and interpret other road users’ gestures and trajectories. In order to interpret them, they will need situational knowledge allowing them to put signals in the correct context. The “official” signals such as turn signals, the horn, and headlight flashing would probably suffice in most situations in order to convey information to other vehicles or road users. Kent Larsen’s research group at MIT proposed an interesting approach for communication between autonomous vehicles and pedestrians [17]. A prototype with a variety of sensors but only remotely resembling an actual car was fitted with a number of devices. Swiveling, blinking LEDs designed to look like an eye turn towards a pedestrian, signaling “I’ve seen you. ”Additionally, directional speakers point towards pedestrians and tell them that it is okay to cross the street. If any pedestrians are identified, color-coded LEDs in the wheels could change from green to yellow to red in order to acknowledge pedestrians or warn them (Fig. 7. 13). Fig. 7. 13:. Image rights: Nicholas Pennycooke, Changing places group, MIT (copyright belongs to author)Full size image Another signal that autonomous vehicles can use to communicate with others is “explicit driving. ”For example, if another vehicle would like to merge and the autonomous vehicle wants to cooperate, the autonomous vehicle should visibly slow down in order to accentuate the gap and assure the other driver that it is safe to merge. Communication and informal rules are in constant flux. To illustrate this point with a verbal example, take the expression that something “sucks. ” Not too long ago, this word was understood as a direct sexual reference, provoking understandable consternation when young people began generalizing it. Similarly, the non-verbal signal “thumbs up” recently spread to countries where it was unfamiliar due to its iconic use in social media. From one angle, that means that autonomous vehicles will need to periodically learn new signals of non-verbal behavior, for instance by regularly updating their visual “vocabulary” along with associated meanings. Yet another effect can also be anticipated. The increased presence in road traffic of autonomous vehicles practicing a distinct, particularly rule-based set of behavior could also encourage other road users to adopt such behavior. This would make traffic more standardized, but not necessarily more efficient. [SEP] Justify:

Output: The text corroborates the claim by discussing the communication abilities of autonomous vehicles and how their rule-based behavior could standardize traffic, which indirectly supports the objective of reducing human error. It highlights that autonomous vehicles need to interpret gestures and signals effectively, implying a design aimed at minimizing misunderstandings that lead to accidents, thus aligning with the claim's focus on reducing human error in transportation.


Input: Task: contrast the claim using the given text. Claim: The GDPR in the EU regulates how companies collect and process user data. [SEP] Text: Commission published a legislative proposal in July 2023, the adopted its position after the first reading in April 2024, and the Council agreed on its general approach on 13 June 2024. Unfortunately, the reform features deeply troubling elements. It seeks to mainstream a controversial Irish approach to dealing with data protection complaints, namely “amicable settlements” that the (DPA) can in some cases decide that have been struck between individuals and digital corporations. Those “settlements” are not always to the benefit of data subjects. Further, and rather problematically, the reform foreshadows the end of the principle of proximity, which was a key part of the GDPR’s enforcement design. Gutting – or at least eroding – the proximity principle should ring alarm bells for anyone concerned with effective judicial remedies in the EU. In this blog post I explain why eroding the principle of proximity and further advancing questionable “amicable settlements” by the Irish DPA would be a catastrophe for European data protection law. In short, the legislator should stop both projects – gutting proximity and mainstreaming “amicable settlements” – in the final trilogues. The principle of proximity allows individuals faced with a GDPR infringement to lodge a complaint with a data protection authority (DPA) of their choice: the authority of the place of the alleged infringement, of their place of work, or of their habitual residence. If the DPA with which they lodged the appeal happens not to be the one competent to take a decision on the case because of the processing’s “cross-border” nature, the local authority will forward the complaint, triggering what is known as the “one-stop-shop” procedure. In these situations, it is the DPA of the of the main establishment of the entity that takes the lead in deciding the case. A data subject living in Brussels who has a problem with Instagram, for instance, can lodge a complaint with the Belgian DPA without having to worry about whether Instagram is based in Belgium or not. DPA shall know that Meta has its main establishment in Ireland and invite the Irish DPA to lead the path towards a decision on the case. That is, actually, only one aspect of the principle of proximity. There is a second important element, connected to the broader picture of effective judicial remedies in the GDPR. [SEP] Justify:

Output: The text contrasts the claim as it discusses proposed reforms to the GDPR that could undermine its current regulatory framework. It highlights troubling aspects of the reform, such as the introduction of 'amicable settlements' that may not benefit data subjects and the potential erosion of the principle of proximity, which is fundamental to how GDPR allows individuals to lodge complaints. This suggests that the regulatory measures designed to protect user data under the GDPR may not be as effective if these reforms are adopted.


Input: Task: pass the claim using the given text. Claim: Adaptive radiation explains the diversification of species in new environments. [SEP] Text: Skip to main content Explore journals Get published About BMC My account Search all BMC 0. 95. Links are drawn to highlight the topological differences. Colours correspond to species clades (see Table 1) as follows: black: outgroups, turquoise: percula, grey: Amphiprion latezonatus and A. chrysopterus (monospecific lineages), orange: clarkii, blue: akallopisos, rose: Australian, green: ephippium, yellow: polymnus, brown: Indian. A coloured legend, which repeats this information for the major clades, is located at the right of the figure. Full size imageFigure 2Homoplasy excess test. Panel (a) shows the distribution of the bootstrap support for each node which showed a homoplasy excess during the removal analysis. Each boxplot shows the null distribution of bootstrap values for the node and the red dot shows the bootstrap value of the node when the taxon of interest (name on the right) has been removed. Nodes names as given on the left correspond to that of panel (b) which shows the consensus tree of the nuclear phylogeny. There, nodes and taxa which showed up as outliers in the analysis have been coloured in orange. Species used as outgroup are shown in grey. The size of node labels has been altered to facilitate reading. Full size imageHybrid signalWe recover an excess of homoplasy at nine nodes of the clownfish phylogenetic tree (Figure 2). Removing either A. ocellaris from Bali or A. percula from Guinea increases the BS of Node 41 (Figure 2), which suggests genetic exchange between members of the percula clade. We find that the “black” specimen of A. clarkii collected in Bali likely originated from hybridisation between other members of the clarkii group (node 37, Figure 2). We measure further increase of BS at three different nodes (6, 16 and 17, Figure 2) when individuals of A. chrysopterus from either Moorea or the are removed. Moreover, the BS of node 17 increases when A. akindynos is removed, which suggests several hybridisation events between A. chrysopterus and species of the Australian and polymnus groups occurring around the timeframe of node 16 and 17. [SEP] Justify:

Output: The text is irrelevant to the claim as it discusses specific phylogenetic analyses and hybridization events among certain species of clownfish without addressing the concept of adaptive radiation or its role in species diversification in new environments. Therefore, it does not support, contradict, or provide any relevant information regarding the claim.


Input: Task: corroborate the claim using the given text. Claim: Autonomous driving technology aims to reduce human error in transportation. [SEP] Text: keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves. 1 MotivationThis paper aims to quantify the effects of autonomous driving on the traffic management level. This involves developing a model of autonomous driving that makes it possible to use human-controlled and autonomous vehicles with only minor modifications. This is important with regard to defining how the instruments of traffic management need to be developed in the future to enable them to handle autonomous vehicles in the transportation system. Of particular interest in this context is mixed traffic, in which normal and autonomously driving vehicles interact with each other. This will presumably be the normal state of affairs on roads for quite some time even after the introduction of autonomous vehicles; it is, therefore, of great practical significance to gain a good understanding of precisely this situation to predict and prevent any systemic effects that may occur. Since such vehicles do not yet exist, portions of the following observations must be regarded as an initial appraisal of possible developments presented as a scenario. However, modeling of human drivers is likewise far from complete, so the focus in this paper will be on establishing consistent modeling. The objective of the modeling presented here is to describe, as far as possible, human and autonomous vehicles with the same model, distinguished only by the different parameters used. A good example of this is the distance to the vehicle ahead expressed in terms of the time gap: an autonomous vehicle can achieve times of 0. 3…0. 5 s [1], whereas vehicles driven by humans are legally required to maintain a distance of at least 0. 9 s (in Germany). The legal recommendation is actually 2. 0 s, but this is seldom maintained except when traffic volumes are low. In heavy traffic, the value is often significantly lower; the figure for heavily traveled autobahns that occurs most often is 1. 1 s (see Fig. 15. 3), with an average value of 1. 4 s. If drivers complied with the legally stipulated specifications, traffic on many roads would come to a standstill much earlier than is currently the case. This paper builds on the papers by Friedrich [2] and Pavone [3] in this book. While [2] describes the general effects of autonomous vehicles on the transportation system, this paper addresses the modeling of autonomous and human-driven vehicles as well as the effects of autonomous vehicles on traffic management. [SEP] Justify:

Output: The text corroborates the claim by discussing the effects of autonomous driving on traffic management and the interaction between human-controlled and autonomous vehicles. It highlights the importance of understanding these interactions to predict and prevent systemic issues, implying that autonomous driving technology is being researched and modeled to minimize human error and enhance traffic flow. Additionally, the mention of parameters like time gaps illustrates how autonomous vehicles can operate more efficiently than human drivers, supporting the notion that the technology aims to reduce human error in transportation.


Input: Task: contrast the claim using the given text. Claim: The GDPR in the EU regulates how companies collect and process user data. [SEP] Text: In contrast, during the same year, in the context of the one-stop-shop it only issued 18 draft decisions and adopted even less final decisions (12 in total). The DPC has in the past discreetly managed to convince the members of the (EDPB) that all this is compatible with the GDPR, by accepting, as a small concession to their peculiar practices, to mark the withdrawal of the complaint by adopting a formal notification. The persistent problem is, however, that – coming back to the example above – this decision to regard the complaint as withdrawn is taken in Ireland, by the Irish DPA, and can only be contested in front of Irish courts. Thus, if the Irish DPA would, for instance, decide that a complaint I lodged with the Belgian DPA is closed (perhaps because I did not react on time, maybe because I did not understand their language, possibly because I never knew my GDPR rights depended on me opening unsolicited communications from distant authorities), and I would wish to contest the validity of their decision, I would have to travel to Ireland and figure out how to launch proceedings there. Just like in 1998. Just like before the GDPR and the EU Charter of Fundamental Rights. This creates a situation in which access to data protection remedies depends on the establishment of the data controller or processor. This goes to the disadvantage of every EU-based Meta user who does not live in Ireland, impacting millions of EU residents. We may end up in a situation which basically caters to large digital companies, rather than individuals. If it goes ahead, the reform threatens the credibility and effectiveness of data protection complaints against large social networks. This goes very much against the idea of the digital single market, and further erodes trust in the free flow of personal data. And, de facto, it grants a joker to all US-based companies that have their main establishment in Ireland. Irish coffee a-go-go The currently discussed legislative proposal on enforcement by further harmonising the procedures related to “cross-border” personal data processing should put an end to existing ambiguities and problematic developments in DPA practices, and make sure that the GDPR is fully respected. Commission fast-tracked its text and published it without even carrying out an impact assessment, arguing that no assessment was necessary because the initiative would not affect the rights of data subjects as set out by the GDPR. [SEP] Justify:

Output: The text contrasts the claim by discussing how the enforcement of GDPR in practice may undermine the regulation's intended purpose. It describes issues related to the Irish Data Protection Authority (DPA) and how decisions made by it can impede individuals' access to data protection remedies. The mention of individuals needing to travel to Ireland to contest decisions undermines the claim that the GDPR effectively regulates user data protection across the EU, indicating potential failures in the regulatory framework that affect EU residents adversely.


Input: Task: pass the claim using the given text. Claim: Adaptive radiation explains the diversification of species in new environments. [SEP] Text: We also find evidence of genetic exchanges between members of the Indian group at nodes 20, 22 and 23 (Figure 2). Finally, we identify gene exchange between the two A. sandaracinos individuals at node 13 and A. pacificus, but the effect on bootstrap values is relatively small. Identification of recent hybrid parental speciesWe infer the phylogenetic trees of mitochondrial (Figure 3) and nuclear (Figure 4) markers for all sampled clownfish species augmented with the additional sequences of the supposed hybrid individuals. The cloning procedure allowed us to separate the two parental BMP4 sequences of each hybrid individual analysed (clones 1 and 2 in Figure 4). As expected, we find that the BMP4 sequences from those hybrid individuals that were cloned do not cluster by individual species but are separated among parental clades (Figure 4). We deduced the maternal origin of the putative hybrids with the mitochondrial phylogenetic tree. We find that both hybrids 2 and 3 inherited their mitochondrial genes from A. akallopisos. The plastid sequence of hybrid 1 is similar to A. perideraion and A. leucokranos, as already proposed [20], derives from A. chrysopterus. Moreover, in the mitochondrial tree, A. leucokranos clusters with A. chrysopterus sampled from the Solomon Islands, the origin of A. leucokranos (Figure 5). The maternal origin of the four hybrid individuals is highly supported in the phylogenetic tree with posterior probabilities of 1 on the respective ancestral nodes (Figure 3). The nuclear tree confirms those results and each individual has one of the cloned sequence that clusters with the same species as in the mitochondrial tree (Figure 4). The position of the other clone provides information on the paternal origin of the hybrids. A. leucokranos clusters with high posterior probability with A. sandaracinos and hybrid 1 and 3 both cluster with A. perideraion. The other hybrid individual (hybrid 3) clusters with any of A. pacificus, A. sandaracinos and A. akallopisos, but low posterior probabilities forbid any definitive conclusion. Nevertheless, we do not find clear evidence of recombination events amongst the cloned sequences, suggesting that we sequenced individuals deriving from recent hybridisation events. Figure 3Mitochondrial phylogeny of hybrid individuals. [SEP] Justify:

Output: The text discusses genetic exchanges and phylogenetic analyses of clownfish species, focusing on hybridization events and mitochondrial sequencing. It does not provide any information related to adaptive radiation or its role in the diversification of species in new environments, making it irrelevant to the claim.


Input: Task: corroborate the claim using the given text. Claim: Autonomous driving technology aims to reduce human error in transportation. [SEP] Text: Such a modeling approach does in any case lead to a very broad spectrum of \( \tau \) values, just as is observed empirically. A second point in which a human differs from a machine is what is known as the action point mechanism [22]. Strictly speaking, a human driver cannot be described by a differential Eq. (15. 1). Rather, a vehicle is controlled through correction of the acceleration (accelerator-pedal position) at irregular time intervals, as shown in the example Fig. 15. 4. Fig. 15. 4Acceleration as a function of time with a human driver. It can be seen that the acceleration changes erratically at the action points. Between the action points, it remains nearly constant. The data was recorded in a “drive” by the author with a driving simulator; similar images can be found in all data records with sufficiently accurate measurement of the acceleration or accelerator and brake pedal. Image rights: copyright resides with authorFull size image The time gaps between successive action points also demonstrate a very broad distribution, with values between 0. 5 and 1. 5 s. Here there is evidently another modeling approach for traffic safety questions—if the time between two action points becomes very long, a critical situation can arise. In normal cases that does not occur, however, and there are only minor variances between a model based on Eq. (15. 1) and a model in which the action points are explicitly used [23]. In particular, the action point mechanism alone does not lead to a wide distribution of gaps between the vehicles. This too is demonstrated in the example used in Sect. 15. 2 of the chain of vehicles following a leading vehicle. An evaluation of the gap measured (in the simulation), here as a function of the number of the following vehicle, shows that in most cases an autonomous vehicle follows the leading vehicle with significantly less variance—in spite of the sometimes extremely volatile behavior. A representation of this is found in Fig. 15. 5. Fig. 15. 5Gap size behavior for human and autonomous vehicles. The graphic shows the average gap and the 25th and 75th percentiles, in each case as a function of the position in the chain. The upper curve is for the model of the human driver and the lower one models a chain of autonomous vehicles. Image rights: copyright resides with authorFull size image Thus the models used in this 0. [SEP] Justify:

Output: The text corroborates the claim by discussing the differences in behavior between human drivers and autonomous vehicles. It highlights that human drivers exhibit erratic and inconsistent acceleration patterns, while autonomous vehicles demonstrate significantly less variance in following distances (gaps) when trailing a leading vehicle. This suggests that autonomous driving technology is indeed designed to be more reliable and consistent than human drivers, which supports the overall aim of reducing human error in transportation.


Input: Task: contrast the claim using the given text. Claim: Data encryption makes online accounts completely unhackable. [SEP] Text: This service is more advanced with JavaScript available Site navigation Search Springerall videos up Continuing in is a preview of subscription content Your browser needs to be JavaScript capable to view this video Try reloading this page, or reviewing your browser settings Autoplay: View previous videoPrevious video View next video View contents of contents This video segment demonstrates the difference between the 2 types of column encryption supported by randomized and deterministic. It then offers advice on how to make a choice when encrypting a column. this video Author(s) First online 27 October 2019 DOI Online ISBN 978-1-4842-5565-0 Copyright information © 2019 Hello and welcome. In this segment, we’re going to look at encryption types. The feature affords us two distinct methods by which to encrypt columns. Coupled with plain text or no encryption, we have three possibilities for each call. It’s important that we understand the difference between the types and the pros and cons of each so that we can make a smart decision when encrypting a column, as we’ll be making this decision on a per column basis. The two types of encryption are called deterministic and randomized. I’ve plotted them on this small chart from plain text as the least secure through randomized as the most secure. Deterministic isn’t really in the middle. It’s certainly closer to the security of randomized than it is plain text. So it being drawn off center is no accident. Before this slide is over, there’s going to be a lot of text on the screen. I’m not planning on reading it all. would encourage you to pause the video and read it if you feel like there is something in there you need to see. The first distinction we’ll look at is that certainly columns that do not contain sensitive data would likely be stored in plain text, while those containing sensitive data would choose one of the two encryption methods. The next big difference is the ability to search the columns. A column encrypted with deterministic encryption may be searched, while one with randomized encryption will not be able to be searched in any way. The searching of a deterministic encrypted column allows for only exact match searches. This would be something with an equal sign or an in clause perhaps but not a like or a less than or between, nothing like that. For that reason, it would also be preferred for primary or foreign key columns. [SEP] Justify:

Output: The text contrasts the claim by discussing the limitations and differences between types of encryption. It explains that while encryption methods improve security (deterministic vs. randomized), they do not guarantee unhackability. Encryption itself introduces trade-offs, such as the ability to search encrypted columns, indicating that data encryption does not make online accounts completely unhackable.


Input: Task: pass the claim using the given text. Claim: Adaptive radiation explains the diversification of species in new environments. [SEP] Text: Previous studies of phylogenetic relationships of the Pomacentridae inferred by mitochondrial or nuclear markers, all support the monophyly of the clownfish as well as the existence of the main clownfish clades [18],[23]-[25]. The topology obtained with the mitochondrial data corresponds to these published phylogenetic trees. We find several deep cytonuclear discordances in the clownfish. The relative position of the ephippium clade changes radically between the two datasets, which suggests the occurrence of hybridisation in the timeframe of node 8 (Figure 2). While this node is not basal to all clownfishes, it is ancestral to 24 of the 28 species (30 described species minus A. leucokranos and A. thiellei which were likely described from hybrid material [20]). This clade represents a major shift in diversification rate with a 2. 5-fold increase compared to the diversification rate of Pomacentridae [22],[23]. The pattern of concurrent increase in rate of diversification and occurrence of hybridisation fits into the framework of the syngameon hypothesis [6], where hybridisation events preludes an acceleration of ecological differentiation and diversification rate. Moreover, this result is robust regarding node support, as the relationship between the clarkii and the ephippium clade is itself highly supported (see Additional file 1). We find paraphyletic mitochondrial genotypes in A. sandaracinos. The fact that the two A. sandaracinos individuals cluster with high support in the nuclear dataset suggests a recent hybridisation event implying a mitochondrial genotype that belongs to an unknown lineage of the akallopisos clade. Results of the HET suggest the ongoing occurrence of genetic exchanges between A. sandaracinos and A. pacificus (node 13, Figure 2). The two species are phenotypically very similar and it is possible that a third cryptic species having the mitochondrial profile of the A. sandaracinos sample from Guinea exists. The remaining topological inconsistencies of the polymnus clade are not well supported as shown by the Bayesian posterior probabilities of their respective nodes on the mitochondrial phylogeny. This discrepancy may be artifactual and is thus not further discussed. Hybridisation and adaptive radiationOur results highlight the existence of a main hybridisation event at the base of the Amphiprion crown group (node 8, Figure 2). [SEP] Justify:

Output: The text does not directly address the claim regarding adaptive radiation and its role in diversification. Instead, it discusses hybridization events among clownfish and their implications for phylogenetic relationships. While it mentions 'adaptive radiation', it does not provide clear evidence or examples that support the claim, resulting in a characterization of the text as irrelevant to the specific assertion.


Input: Task: corroborate the claim using the given text. Claim: Autonomous driving technology aims to reduce human error in transportation. [SEP] Text: Image rights: copyright resides with authorFull size image With maximum demand, the system extends the green times up to the limit of 40 s and thereby demonstrates that it has reached its saturation level. However, this only applies for a flow of normal vehicles. As soon as autonomous vehicles are added to the mix, the top delay value for sinks and with an equipment rate of 50 %, the maximum green time is not even reached. This lines up with the observation in Sect. 15. 4 that autonomous vehicles not only increase the capacity, but also contribute to a reduction in green times—an effect that is rather clear in this example, namely that even a small proportion of autonomously guided vehicles can make a noticeable impact. 6 with Autonomous VehiclesThe previous scenarios examined a single intersection. Much more interesting is the case of a stretch of road with multiple intersections in succession which are all controlled by a traffic signal system. In this case the coordination between the traffic signals, known colloquially as the green wave, plays an important role. Here again, a simulation is used to investigate how great an impact the introduction of autonomous vehicles has. Analogous to the procedure in [27], a section of road with 10 intersections is simulated with varying coordination configurations. The demand, which is constant, the green times and the cycle times remain unchanged. The only change is to the offset, i. e. the point in time at which the traffic signal turns green for the vehicle flow in a particular direction. If this offset between two signals is precisely equal to the travel time between the two signals, the system is in its optimal state: the delay for the vehicles at the downstream signal is exactly zero when the green times are equal. In that case, just as many vehicles can cross the intersection as left the upstream traffic signal. The expectation is clear: In this case, no improvements will be achieved with an autonomous vehicle; and that is precisely what the simulation results in Fig. 15. 8 demonstrate. However, autonomous vehicles do indeed improve the delay times in the case of sub-optimal coordination. The reason is that the bunch of vehicles that leaves a traffic signal is more compressed than with human drivers. Fig. 15. 8Delay as a function of the offset time for a simple green wave. Depicted here is a simulation with human drivers (gold) and one only with autonomous vehicles (blue). Shown here is the best result achieved between the first and second intersection. [SEP] Justify:

Output: The text corroborates the claim by illustrating that autonomous vehicles can reduce delays at intersections through better vehicle coordination, which allows for a more efficient flow of traffic compared to human drivers. This aligns with the idea that autonomous driving technology seeks to minimize human error, particularly in managing vehicle movement and traffic signals.


Input: Task: contrast the claim using the given text. Claim: Data encryption makes online accounts completely unhackable. [SEP] Text: If a column needs to be searched completely, it has to be left in plain text. Randomized is also preferred for columns with low cardinality. That is columns with a small universe of values. We’ll look at an example of that in a moment. There’s a series of data types that are not eligible to be encrypted. And those all must be done in plain text if they’re to stay. There are also a series of properties about columns that make them ineligible for encryption. So those two types of encryption are called deterministic and randomized. Deterministic is a repeatable encryption. This means that when a cryptographic operation takes place, any given input value will consistently return the same cipher text value. With randomized encryption, this is not true. You could give it the same input value again and again and it will consistently get different cipher text values. That makes for the stronger encryption. On the plus side, a deterministic encrypted column is indexable. This is precisely because it is repeatable. A client machine can encrypt a value to be searched, send that Cipher text to the server, and ask SQL Server to find the cipher text without SQL Server ever knowing what the underlying value is being searched. With randomized encryption, any search of the column, even an exact match, will result in an error. On the downside for a column encrypted with deterministic encryption, it is much less secure on a column of low cardinality. That is a column with a small universe of possible values. A good example of this might be a gender column. There are probably only two values available for that column. And even if the application is more liberal and allowed more options, you would still expect the bulk of the data to fall into one of two values. Let’s see some examples. In this table, we have a list of orders and each order is assigned to a person. And we’ve assigned it to them using their government ID. And I’ve made these look like American social security numbers. Because the deterministic encryption always spits out the same value, we can see that this person has three orders. And each of their three orders gets the exact same cipher text. Even though SQL Server has no idea what the underlying value is and even though someone without access to the appropriate keys doesn’t know what that is, they can tell that that person has three orders. [SEP] Justify:

Output: The text contrasts with the claim by explaining the limitations of data encryption, particularly with deterministic encryption, which can be less secure for low cardinality columns. It illustrates that while encryption can obscure data, it does not make accounts completely unhackable, as someone can infer information based on the consistency of cipher text outputs, particularly with deterministic encryption.


Input: Task: pass the claim using the given text. Claim: Adaptive radiation explains the diversification of species in new environments. [SEP] Text: Further studies are required to measure the extant of ongoing hybrid speciation, for example in A. leucokranos. There, field observations coupled with experiments will allow to identify the conditions in which hybrids have a better fitness than their parents, illustrating the thight relationship linking species ecology and evolutionary processes. MethodsPhylogenetic reconstructionWe used previously extracted DNA from 41 individuals (see [19] for details; Additional file 3: Table S1) representing 27 species of clownfish to analyse the evolutionary history of mitochondrial genes. We amplified parts of the ATP86, CytB and 16S markers via polymerase chain reaction (PCR) with a 25 μl reaction mixture containing ~50 ng of DNA template, 2. 5 μL GoTaq buffer, 3 μL dNTPs (2. 5 μM), 1 MgCl2 (25 μM), 1 μL of both forward and reverse primer and 0. 3 μL of Taq polymerase (). Primer sequences and PCR cycles were set as in [24] for ATP86 and CytB, and [25] for the 16S. We purified the amplification products with the (Qiagen, GmbH, Germany). We sequenced the purified products forward and reverse strands with the 3. 1 Terminator cycle sequencing kit (), according to the manufacturer’s instructions, and separated the products on an ABI Prism 3100 genetic analyser (). We assembled a nuclear dataset from sequences generated in a previous study [19]; Additional file 3: Table S1). The nuclear dataset is 6,679 base pairs long and comports seven genes (BMP4, Glyt, Hox6, RAG1, S7, Svep, Zic1). We used MAFFT 6. 864b [36] with default settings to align each mitochondrial and nuclear gene individually. We verified each alignment visually and concatenated the mitochondrial and nuclear datasets in two separate super-matrices with SequenceMatrix [37]. We estimated the mitochondrial and nuclear phylogenetic relationships of clownfish species with MrBayes 3. 2. 1 [38]. The parameters of the nucleotide substitution process for each gene in the mitochondrial and nuclear data sets were sampled across the GTR model space during the Bayesian MCMC [39]. This approach has the advantage of removing the need of a priori testing for a specific model of substitution (e. g. [40]). [SEP] Justify:

Output: The text discusses the methodologies used for studying the evolutionary history and relationships among clownfish species, but it does not address or provide evidence related to adaptive radiation or its role in the diversification of species in new environments. Therefore, it is irrelevant to the claim.


Input: Task: corroborate the claim using the given text. Claim: Autonomous driving technology aims to reduce human error in transportation. [SEP] Text: 5 s means that the time-step size in SUMO also has to be set to 0. 5 s to ensure that the vehicles can continue to drive without colliding. This extends the simulation time from around 50 min to 90 min for the simulation of an entire day in Braunschweig. Only the passenger vehicles were simulated as autonomous vehicles; the approximately 44,000 trucks remained unchanged. The traffic signals were likewise not entirely correctly represented in the simulation. It may therefore be assumed that on this end as well, further corrections to the simulation results below can be expected. Nevertheless, this simulation delivers significant preliminary results, as seen in Fig. 15. 10. Even without further measures, the autonomous system is more efficient in the sense that it reduces delays between 5 and 80 %, with an average value of around 40 %. With the selected parameters, however, the variance in travel times changes relatively little; the system, in other words, becomes faster, but not necessarily more reliable. That could change if the traffic management system were also realistically simulated. Such studies are currently in the works. Fig. 15. 10Comparison of the delays for a simulation with human drivers (gold, upper curve) and a simulation in which the passenger vehicles drive autonomously (blue, lower curve). Each data point is a floating average value from the 8 adjacent one-minute values. The dispersion of the values of the two curves is not very different and is therefore not displayed. Image rights: copyright resides with authorFull size image 8 ConclusionThis paper presents some initial considerations regarding how traffic management needs to respond to the opportunities presented by autonomous driving. The case studies presented here demonstrate that, depending on the scenario, very different improvements can be achieved in the flow of traffic through the introduction of autonomous vehicles. Unfortunately, the improvements that could be achieved are difficult to summarize with a single number. It was demonstrated in Sect. 15. 4, for example, that the capacity of a traffic signal can certainly be doubled. If the demand is low at the corresponding signal, this doubling is scarcely noticeable. But if the signal is working at the limits of its capacity, by contrast, even a minor increase in its capacity can lead to a dramatic improvement. This can be observed quite clearly in the scenario in Sect. 15. 5: here the demand runs the values from very low to (temporary) over-saturation. [SEP] Justify:

Output: The text corroborates the claim by indicating that autonomous vehicles improve traffic flow efficiency and reduce delays by 5 to 80%. This suggests that by enhancing the management of traffic and minimizing human error in driving, benefits such as improved reliability and effectiveness can be achieved, particularly under conditions of high demand at traffic signals.


Input: Task: contrast the claim using the given text. Claim: Data encryption makes online accounts completely unhackable. [SEP] Text: We don’t know who it is, but we know that they have three. We can also sort by that value, making the data looks something like that. This is sorted by the government ID cipher text. It’s very easy to imagine that an application could take that value, 123-45-6789, using the repeatable encryption, they can turn that into the 0x1352 cipher text and ask SQL Server to find 0x1352, which it would do very quickly. Notice though that even though this is an index on government ID, they’re not sorted by the plain text. So it can help us find this exact match value. But if we ask it to do something like a value that was like 1% or something that was less than nine, anything like that that would be considered an inequality search, this index is not helpful to us. It is only helpful for an exact match search, meaning an equal sign or an in type of value. What would this table look like with randomized encryption? Here, we have the same table with the same orders and the same government IDs. But when we look at our repeated input values, each one gets a different and distinct cipher text. Now someone looking at this cipher text can’t even determine that one person had three orders. But now any search run against this government ID column, exact match or otherwise, will result in an error, as the client given an input plain text value cannot possibly determine every randomized value available. That example shows a higher cardinality table. Let’s look at a lower cardinality example. Here, we have two copies of the same table. On the left, we have encrypted the gender column using deterministic encryption and on the right randomized. On the left, we can see that each person who has a gender M receives the exact same 0x29 cipher text. And everyone with an F gets the 0xc63 cipher text. It would not take someone looking at that cipher text very long to realize that one of those values was M and one of those values was F. And if they were to crack even one row by finding something else about that value that suggests which one is M or F, they could then figure out every single value. On the right, however, every M and every F gets a separate cipher text. [SEP] Justify:

Output: The text contrasts the claim by illustrating that certain types of encryption can be vulnerable. It explains how deterministic encryption can expose patterns in the data, allowing someone to deduce values (e.g., gender associated with certain cipher texts), which could lead to a potential hack. The discussion on randomized encryption further demonstrates that while it provides more security by obscuring data patterns, it still shows that no encryption method is foolproof, especially if improper types are used.


Input: Task: pass the claim using the given text. Claim: Adaptive radiation explains the diversification of species in new environments. [SEP] Text: We performed, for each dataset, two independent runs of MrBayes with four parallel MCMC chains, each 10 million generations long, and sampled model parameters every 1000 generations. We checked for optimal parameter convergence using the ‘sump’ command and by visualising the trace files and the ESS values of the MCMC chains using Tracer v 1. 5 [41]. After the removal of 25% of the trees as burn-in, we merged the two independent runs to generate a majority-rule consensus tree for both mitochondrial and nuclear dataset that included all compatible groups. Hybrid signalWe assessed cytonuclear discordance by comparing visually the topology of both consensus phylogenetic trees. This step was facilitated by the use of the ”cophyloplot” function available in the R package Ape [42]. We further applied the framework of the HET on the nuclear dataset only [6]. Hybrid taxa are intermediate to the parental species when considering their nuclear genome, but they share identical alleles at each individual locus with only one parent [43]. When included in a phylogenetic tree based on a matrix of concatenated nuclear markers, a hybrid taxon will introduce an excess of homoplasy due to the conflicting phylogenetic signal present in the corresponding gene tree topologies in the clades containing the two parental species. Thus, the removal of a putative hybrid should reduce conflicts in the phylogenetic tree and result in increased support values for the nodes that includes the parental species [6]. While AFLP have initially been proposed as input data for the HET, it is conceptually similar to use the alignments of several nuclear genes. The only drawback of using sequence data is a reduction in power due to the lower amount of data and less independence between sites. Yet, by increasing the number of genes studied, this should not be a major issue. We implemented this test in R 3. 0. 1 [44] and used RAxML 7. 3. 5 [45] to perform the phylogenetic inference and bootstrap support (BS) using the ‘’ model of nucleotide substitution and specified a different partition for each gene in the dataset. For a species i, we first removed it from the complete concatenated alignment of nuclear data. We used this reduced data set lacking species i to build a phylogenetic tree and estimate node support with 100 bootstrap replicates. [SEP] Justify:

Output: The text is irrelevant to the claim as it discusses methodological aspects of assessing phylogenetic trees, including the use of MrBayes and the challenges posed by hybrid taxa. It does not provide any information or evidence related to adaptive radiation or its role in species diversification in new environments.


Input: Task: corroborate the claim using the given text. Claim: Safety is a primary focus in the development of autonomous vehicle technology. [SEP] Text: of and Technology > Vol. 13 No. 3, September 2023 Compare and Contrast LiDAR and Non-LiDAR Technology in an Autonomous Vehicle: Developing a () Benjamin Quito, School of Computing and. DOI: 10. 4236/ojsst. 2023. 133006 123 Downloads 609 Views 94. 63% in predicting the images. Secondly, a is established to facilitate the subsequent proposal for Experimental Research, which is the third aspect. Framework incorporates the application of LiDAR and Non-LiDAR technologies mounted on a vehicle that is operated in diverse weather conditions by both a human driver and an autonomous system. tables, which are currently in a blank state, will be populated upon completion of the Experimental Research. Research aims to compare and contrast the performance of LiDAR and Non-LiDAR technologies on a vehicle driven by a human operator versus an Autonomous Vehicle. The findings of this research will be documented in the Scenario table outlined within this study, ultimately shedding light on the safety implications of implementing LiDAR and Non-LiDAR technologies within an AV context. -LiDAR Share and Cite: Quito, B. and Esmahi, L. (2023) Compare and Contrast LiDAR and Non-LiDAR Technology in an Autonomous Vehicle: Developing a Safety Framework. of and Technology, 13, 101-131. doi: 10. 4236/ojsst. 2023. 133006. 1. idea of an (AV) started with Vinci when he designed a self-propelled cart that could move without being pushed [1]. Google started its project in 2009 [2]. In 2015, Tesla released its autopilot [3]. Tesla’s autopilot is not entirely AV; the feature is Autosteer that keeps the car in its current lane; the technology has four different kinds of sensors; forward radar, forward-facing camera, 360-degree ultrasonic sensors, and GPS in combination with high-resolution navigation maps designed to track individual lanes and features of the roads [4]. NHTSA further mentioned that self-driving vehicles would be integrated into U. S. roads by advancing through the six levels of driver assistance technology advancements. The six-level driver assistance technology advancements focus on who does what and when, i. e. , the driver and the systems (ADS). 1. 1. [SEP] Justify:

Output: The text corroborates the claim by indicating that the research aims to 'compare and contrast the performance of LiDAR and Non-LiDAR technologies on a vehicle driven by a human operator versus an Autonomous Vehicle' and that the findings will 'ultimately shedding light on the safety implications of implementing LiDAR and Non-LiDAR technologies within an AV context.' This shows that safety considerations are being actively addressed in the development of autonomous vehicle technology.