Input: Task: pass the claim using the given text. Claim: Priming can affect behavior without conscious awareness. [SEP] Text: 7554/eLife. 09651. 009 Importantly, c – the contrast or evidence level along each axis that gave rise to the data sample the observer sees – is unknown to the observer. So, because contrast evidence is a secondary (or nuisance) variable to the primary variable of interest – in this case, the orientation of the Gabor patch – the observer ‘integrates out’ or marginalizes over all possible contrast evidence levels to produce the posterior probability estimate of each tilt (Yuille and Bülthoff, 1996). Thus, the joint probability of each orientation and contrast evidence level is estimated through Bayes’ rule (2) p(S,c|d) = p(d|S,c) p (S,c)p (d) and then the secondary variable is integrated out, leaving estimation of the posterior probability of each orientation S via the marginal distribution (Yuille & Bülthoff, 1996) (3) p (S|d) = ∫ p (S, c|d)dc In the simplest form, both orientations have equal prior probability of 0. 5. The observer then makes its orientation decision (for each interval) via (4) Schosen = arg maxi p determine which interval’s choice the observer is more confident in, the model refers to the magnitude of the posterior probabilities of each Schosen in each interval as a measure of the probability of having made a correct orientation discrimination choice, i. e. p(correct) = p(Schosen|d). Then, the observer compares these posterior probabilities for the target-present (TP) and target-absent (TA) intervals by computing a decision variable D via (5) D = log(p(Schosen,TP|dTP)p(Schosen,TA|dTA)) The observer bets on the interval with the higher probability of being correct: if this decision variable D is greater than 0, the observer selects the target-present interval to ‘bet’ on; if it is less than 0, the observer selects the target-absent interval. Sample code for this Bayesian ideal observer is included in Source code 1. [SEP] Justify:

Output: The text does not address the claim regarding priming and behavior. It discusses a Bayesian model related to decision-making and posterior probabilities but does not mention priming or its effects on behavior, thus being irrelevant to the claim.


Input: Task: corroborate the claim using the given text. Claim: Climate change is causing more extreme weather events worldwide. [SEP] Text: 58% for all natural disasters and approximately 260% for Q4 natural disasters, connecting climate change tweet volume increases with concurrent extreme weather events. Table 2. Climate change media around major natural disasters from 2010 through 2020. Increases are calculated in comparison with daily averages for the 15 days prior to each disaster. “Q4 disasters only” refers to the subset of natural disasters in the highest quartile of media coverage. Positive values indicate an increase, and negative values indicate a decrease. Climate change news volume exhibited similar increases on average around major natural disasters from the NOAA dataset: mean volume percent increases between the baseline period (15 days prior to each event) and the start date, end date, date range daily average, and peak volumes of each event, were 6. 92%, 9. 34%, 8. 10%, and 62. 81%, respectively (Table 2). These increases were once again dwarfed by mean Q4 natural disaster event increases, which measured 10. 25%, 23. 17%. 14. 72%, and 70. 40% at the same time points. Therefore, between 2010 and 2020, on average, climate change media volume increased dramatically around natural disasters, particularly for events that sparked above Q4 natural disasters tweet volume. However, the ratio of climate change tweets reflective of belief in global warming (positive sentiment) to tweets reflective of negative sentiment tended to decrease around natural disasters: mean percent changes from the average 15-day pre-event-date-range sentiment ratios were approximately −3%, −10%, and −7% to the start date, end date, and daily average ratios for major natural disasters. 3) Correlations between media types around major natural disasters Although correlation coefficients of climate change and natural disaster media volume were generally low, they increased considerably around large natural disasters (Fig. 3). On average, between 20 and 30 days prior to an event’s date range, the correlation between natural disasters tweet volume and climate change tweet volume was approximately 0. 21, exhibiting little change over that interval. The correlations then assumed an approximately normal-shaped distribution, reaching a maximum of 0. 3 (approximately 40% increase) at an average of 2 days after each event, before returning to a stable correlation of roughly 0. 25. The correlations between climate change and natural disasters news volumes were slightly higher, starting with an average baseline value of roughly 0. 51 (20–30 days prior to the event). [SEP] Justify:

Output: The text corroborates the claim by detailing the significant increases in climate change media volume around major natural disasters, suggesting a connection between climate change and extreme weather events. For instance, it mentions that 'climate change media volume increased dramatically around natural disasters' and that correlations between climate change and natural disasters media volume increased considerably during such events. This indicates a relationship supportive of the claim that climate change is linked to more extreme weather occurrences.


Input: Task: contrast the claim using the given text. Claim: A vegan diet is automatically healthier than other diets. [SEP] Text: *Data values are not presented when the percentage was less than 5%Full size imageNutrient adequacy of selected dietary patternsThe estimated energy intake of the average diet of Australians aged 19–50 years was 9191 kJ, compared to 11421 kJ for the modelled ADGs diet and 10242 kJ for the modelled PHRD (Table 3). The following analysis is a comparison of the nutrient composition of the selected dietary patterns to the Australian nutrient recommendations; the Requirements (EAR) or the (where no EAR was available). Sodium intake was compared with the Target (SDT). For the 22 nutrients examined, intake was compared with the appropriate nutrient reference value and expressed as a percent. Table 3 Estimated environmental impact and daily average nutrient intake of Australian adults aged 19–50 years stratified by levels of meat and dairy intake, and indicators of a healthy diet (highest vegetable, lowest discretionary food and beverage consumption, and highest overall diet quality) and compared to the Guidelines and the Reference Diet. Values are presented as kilojoules for energy and as a percentage of the nutrient reference value for all other nutrientsFull size tableThe ADGs, as designed, provided more than 100% of the EAR or AI for all the nutrients assessed, and among the selected diets, the highest diet quality pattern, being the one closest to the ADGs recommendations, met the nutrient reference values for 21 out of 22 nutrients examined. Like all the selected dietary patterns, the highest quality diet exceeded the SDT for sodium. The PHRD also met 21 of the 22 for the nutrients assessed, falling short of the EARs for calcium (71% of EAR). Selected diets developed based on lower or higher intake of one food group tended to be most at risk of not meeting the nutrient reference values. The lowest meat and lowest dairy patterns met the EARs for 16 and 17 of the 22 nutrients assessed, respectively, with dietary fibre, calcium, potassium, linoleic acid below the EAR and sodium exceeding the SDT for both dietary patterns. Zinc was also below the EAR for the lowest meat diet. The nutrients most commonly at risk of insufficiency in the selected Australian dietary pattens were dietary fibre, calcium, potassium, and linoleic acid, with sodium at risk of excessive intakes. Environmental impacts of selected dietary patternsThe combined environmental index value for the average Australian diet was 0. 13 (Table 3). [SEP] Justify:

Output: The text contrasts the claim by providing a detailed analysis of various dietary patterns among Australians, indicating that selected diets based on varying levels of meat and dairy intake have different nutrient adequacy. It states that the lowest meat and lowest dairy diets met only 16 and 17 of the 22 nutrients assessed, respectively, suggesting that these diets may not fully meet nutrient requirements. This information points to the complexity of dietary health rather than supporting the idea that vegan diets are automatically healthier.


Input: Task: pass the claim using the given text. Claim: Priming can affect behavior without conscious awareness. [SEP] Text: About half of respondents (52%) received their degree in the last five years, and just over a third (38%) reported having one or more relevant publications on subliminal/unconscious perception. See 10. 7554/eLife. 09651. 011 10. 7554/eLife. 09651. 012 Most respondents reported believing that subliminal processing exists (Q1) (94%), and that the subjective threshold is above the objective one (Q2) (89%). Of those who responded ‘Yes’ to Question 2, however, only about a third (36%) reported believing Performance without Awareness has been unequivocally demonstrated in the literature (Q3) (2. 2. 2. We also found that subjects were overall unbiased in their choices of left- versus right-tilted Gabor patches, justifying use of percent correct rather than the signal detection theory metric d’. We calculated the mean criterion used by each subject across all levels of contrast according to signal detection theory, assuming equal variance for distributions representing left- versus right-tilted stimuli, i. e. (A2-1) c = -12zHR + zFAR where HR refers to the hit rate (i. e. , the participant said ‘left’ when a left-tilted Gabor was presented) and FAR refers to the false alarm rate (i. e. , the participant said ‘left’ when a right-tilted Gabor was presented) (Macmillan and Creelman, 2004). Individual subjects’ criterion values are presented in 1. A two-tailed t-test (treating each subject as an independent sample) revealed these values to not be significantly different from 0 (10. 7554/eLife. 09651. 016 ExperimentSubjectc110. 04720. 0743-0. 0394-0. 0295-0. 0136-0. 03070. 02678-0. 03990. 003210. 22920. 00430. 035Mean (σ)0. 022t(11)1. 04p0. 3215 2. Subjects were able to detect the target in the TP interval above chance as soon as they were able to discriminate the target above chance, and individual subjects’ data closely resembled the group data (Figure S3). [SEP] Justify:

Output: The text does not directly address the claim about priming affecting behavior without conscious awareness. Instead, it discusses respondents' beliefs about subliminal processing and reports on a study involving left- and right-tilted Gabor patches, which does not provide evidence or insights regarding the specific claim about priming.


Input: Task: corroborate the claim using the given text. Claim: Climate change is causing more extreme weather events worldwide. [SEP] Text: News volume correlations also assumed a roughly normal-shaped distribution, reaching a peak value of 0. 65 (approximately 30% increase), 3 days after each event. Despite overall somewhat normal distributions, the average media volume correlations in the actual natural disaster date ranges themselves were low (relative to the overall distribution shape), likely as a result of specific outliers. Regardless, correlations between climate change and natural disasters news and tweet volumes increase dramatically around extreme weather events. The average correlations between climate change sentiment ratio (number of positive tweets/number of negative tweets) and natural disasters tweet volume were also calculated and exhibited a far steeper increase than the aforementioned plots: the starting, baseline correlation value was 0. 02, growing to a peak of roughly 0. 12 (500% increase) 2 days before each event, on average. Although these correlation values are extremely low, the drastic increases around major natural disasters assert the influence of extreme weather events on public attitudes toward climate change. Size Fig. 3. Volume correlations around major natural disasters: correlations between climate change and natural disaster (left) tweets and (center) news volume around specific major natural disasters in the NOAA dataset and (right) correlations for climate change tweet sentiment ratios (positive/negative tweets) and natural disaster media volume. The red bars in each of the figures represent the average correlations across NOAA natural disaster date ranges (and 1 day on each side). The average correlations before disasters (to the left of the red line) were calculated from values between 30 days and 2 days before the start of every natural disaster. Average correlations after disasters were calculated from values immediately after the start of natural disasters to the specific number of days after a disaster that each bar represents (up to 30 days after the start of a given disaster). Citation: Weather, Climate, and Society 15, 1; 10. 1175/WCAS-D-22-0053. 1Download FigureDownload figure as PowerPoint slide 4) Corresponding increases between climate change and natural disaster media As stated above, correlation values of climate change and natural disasters media volume were generally quite low, perhaps partly as a result of the two variables’ tendency to increase/decrease at different speeds (a slightly nonlinear relationship). To control for this and focus analysis solely on the relationship between increases in both variables, standard deviation and percent increases for climate change media volume were computed and compared with corresponding natural disaster media volume standard deviation increases, on a day-to-day basis. [SEP] Justify:

Output: The text corroborates the claim by discussing the correlation between climate change and extreme weather events. It highlights that correlation values for climate change and natural disasters tend to increase dramatically around extreme weather events, suggesting an influence of these events on public attitudes toward climate change. This implies a relationship between climate change and the occurrence of extreme weather, supporting the assertion of increasing extreme weather events due to climate change.


Input: Task: contrast the claim using the given text. Claim: A vegan diet is automatically healthier than other diets. [SEP] Text: The environmental impact value for the ADGs modelled using foods with lower environmental impacts was 31% lower, and the PHRD 46% lower than the average Australian diet. Among the selected Australian diets examined here the diets with least meat and dairy had the lowest environmental impacts. Given there is a relationship between dietary energy and dietary environmental impacts, comparing diets of similar kilojoules can highlight the impact of food choices on dietary environmental impacts. When the environmental impact index value for each diet was adjusted to a standardised 10,000 kJ, the rank order of the diets changed slightly, however the modelled ADGs diet and the PHRD still had the lowest environmental impacts (Table 3). That said, iso-caloric comparisons need to be interpreted with care since food choice and total energy intake are not independent. DiscussionThis paper compared the daily food group recommendations contained within in the (PHRD), a global set of dietary recommendations, to those set out in the Guidelines (ADGs) and various dietary patterns exhibited by the Australian population. The PHRD was found to differ from Australia’s national dietary guidelines considerably in relation to the proportion of the diet provided by the meat and alternatives food group. This difference was due to the higher suggested intake of plant-based alternatives including legumes and nuts which made up 80% of the total amount of meat and alternatives recommended. The PHRD does not include discretionary choices but rather contains an allowance for saturated fats and added sugar. When converted to servings of discretionary foods, the ADGs contained about twice as many servings of discretionary foods as the PHRD. The environmental index impact values for these two benchmark diets were 31–46% lower than for the typical Australian diet. The comparison of selected dietary patterns among Australians to a set of global and national benchmarks in terms of food group composition, nutrient adequacy and environmental impacts provides useful insights into the complexities of population level nutrition advice to improve the health of our people and the planet. The role of national dietary guidelines is to support their population to make healthier food choices, in a balance that promotes human health and more recently environmental sustainability [1, 6,7,8]. However, this analysis showed that the average Australian adult diet contained at least one less serving of vegetables, dairy foods, grains and in particular wholegrains, and unsaturated fats than what is recommended for health and wellbeing in the Australian national dietary guidelines. [SEP] Justify:

Output: The text contrasts the claim by detailing comparisons between various dietary patterns, including the Australian national dietary guidelines and the PHRD. It highlights that diets with lower meat and dairy had lower environmental impacts, yet it advises caution in interpreting iso-caloric comparisons as food choice and energy intake are interdependent. Moreover, it notes that the average Australian diet has deficits in recommended servings of vegetables and wholegrains compared to guidelines, suggesting that a vegan diet (or one that emphasizes plant-based foods) may offer health benefits; however, it does not assert that a vegan diet is automatically healthier than all other diets.


Input: Task: pass the claim using the given text. Claim: Priming can affect behavior without conscious awareness. [SEP] Text: Note that the computational model described in the main text does not apply to these data, as participants were doing 2IFC detectability discrimination rather than a 2IFC metacognitive judgment. This means that even though participants’ data lie near the identity line of equal orientation percent correct and percent betting on the TP interval (dashed diagonal line) – above the behavior predicted by the confidence model – this should not be taken to mean they are displaying any sort of ‘supra-optimal’ behavior, as the tasks are fundamentally different. The position of responses on this graph for this task will depend on the precise relationship between the detectability and discriminability of the stimulus for each individual subject (Thomas et al. , 1982; Thomas, 1987). 10. 7554/eLife. 09651. 017 10. 7554/eLife. 09651. 018 Formally, instead of cright = [0, c], we define (A4-1) cright = c·cosθ,c·sinθ The θ parameter thus allows control of the degree of correlation (or anti-correlation) between the Sleft and Sright distributions. This formulation is mathematically equivalent to specifying non-zero covariance in the definition of the variance-covariance matrix Σ (Macmillan & Creelman, 2004), but we favor using θ here because it provides a graphically intuitive analog to changing the amount of ‘tilt’ between the left- and right-tilted Gabor patch targets, that is, the relationship between the detectability and discriminability of a stimulus (see also King and Dehaene, 2014). For example, Gabor patches that are ± 45° tilted from vertical ought to demand a larger θ value than other, less discriminable versions, such as ± 5° tilt. To explore whether this factor was important to model fits, we fitted the value for θ to the three participants’ data who had completed both main experiments (see Comparison of model to behavioral data, below) and compared model fits with measures of R2 (see Goodness of fit, below) and the Bayesian information criterion (BIC). Best-fitting values for θ were quite consistent across participants, and indicate a slight anti-correlation between the Sleft and Sright distributions (μ = 97. 27º, σ = 18. 79º). [SEP] Justify:

Output: The text does not address the claim about priming affecting behavior without conscious awareness. Instead, it delves into specifics regarding a computational model of psychophysical discrimination tasks and comparisons of model fits with behavioral data. Therefore, it is classified as irrelevant (pass).


Input: Task: corroborate the claim using the given text. Claim: Climate change is causing more extreme weather events worldwide. [SEP] Text: Correlations were then calculated for the relationships between these increases of both variables, yielding more meaningful values. When calculated for values in 5- and 15-day intervals around specific major natural disasters only, these correlations (between climate change tweet percent increases and natural disaster tweet standard deviation increases) were 0. 907 and 0. 914, respectively; overall correlation was 0. 720 (Fig. 4 and Table 3). Climate change news volume percent and natural disaster news volume standard deviation increases exhibited similar high correlations of 0. 909 and 0. 910, for values within 5- and 15-day intervals of specific disasters, and 0. 838 overall, for the entire study interval. Size Fig. 4. Climate change media correlation with natural disaster media increases charts, showing the relationship between increases in natural disaster news and tweet volume and corresponding increases in climate change tweet and news volume metrics (percent increases and standard deviations). Blue lines correspond to the relationships overall across the duration of the study’s time interval (2010–20). Green lines represent only the values present within 15 days of specific major natural disasters in the NOAA dataset, and red lines have the narrower time interval of 5 days surrounding specific disasters. Shown are (top) increases in tweet volume, (middle) increases in news 10. 1175/WCAS-D-22-0053. 1Download FigureDownload figure as PowerPoint slide Table 3. Correlations and models for climate change media and natural disaster media volume increases. Correlation values were calculated between the specified metric for climate change–related media (news and tweet volume), and standard deviation increases in natural-disaster-specific news 3. The first two columns of values represent the tweet volume data, whereas the rightmost two columns represent values for news 4. Interestingly, each news model had roughly 2 times the slope of its tweet model counterpart: 7. 86–3. 92, 6. 04–3. 06, and 2. 73–1. 33 for the 5-day, 15-day, and overall intervals, and, across each of the figures, smaller intervals almost always resulted in higher model slopes (5-day intervals around specific disasters models usually had the highest slopes, while the overall interval relationship trends had the lowest slopes across all analyses). [SEP] Justify:

Output: The text corroborates the claim by presenting statistical correlations between increases in climate change-related media and natural disaster events. It highlights a high correlation (0.907 and 0.914) between the prevalence of climate change tweets and increases in natural disaster tweets, which suggests an association between climate change and the occurrence of extreme weather events. The data indicates that as climate change news volume increases, there is a corresponding increase in the media coverage of natural disasters, reinforcing the idea that climate change may be linked to more extreme weather occurrences.


Input: Task: contrast the claim using the given text. Claim: A vegan diet is automatically healthier than other diets. [SEP] Text: Some subgroups of the population, such as those adults with the highest vegetable intake, did achieve the daily recommendations for vegetables, however these adults did not necessarily consume the recommended amounts for all the other food groups that form part of a healthy diet according to the ADGs. Likewise, those with the lowest intake of discretionary foods and beverages did not necessarily consume adequate amounts of the healthy five food groups. Animal-based food groups such as dairy and meat, and in particular red meat, have higher environmental impacts per serving compared to other foods [34]. diets lowest in these foods had lower environmental impacts than other selected Australian diets, the overall consumption was below many of the food group recommendations for health, and subsequently these diets were among the least nutritionally adequate dietary patterns analysed here. Focusing on single food groups or nutrients has been the basis of some nutrition advice in the past, however, there have been calls to move away from this reductionist approach towards more of a whole-of-diet or dietary pattern approach [38,39,40] and our results support this. The ‘highest diet quality’ pattern was closest to the ADGs recommendations without necessarily achieving full compliance with the guidelines (average score 62 out 100). So, while not necessarily ideal or the healthiest possible diet, it was pleasing that this pattern achieved the estimated requirements for all nutrients examined, except sodium. Importantly this dietary pattern provides an example of an actual way some Australians are eating (1,928/5,920; 33% of adults 19–50 years), which if adopted by more people, could result in improvements in diet quality and health. However, the estimated environmental impact of this pattern of eating was similar to that of the average Australian diet, so further changes in food choices within food groups would be required in order to achieve improved healthiness and reduced environmental impacts compared with the average Australian dietary pattern. There is consensus that diets need to change but achieving significant change has been a long-standing public health challenge. There are also growing calls for transformation of the food system [41] so that everyone can eat a nutritious diet of healthy foods produced from a food system that is environmentally, socially, and economically sustainable [41, 42]. It is also a challenge to balance public health and individual nutrition goals with environmental objectives [42]. One starting point for change is in dietary guidelines and ensuring these documents and their advice acknowledges the connections between the dimensions of food, health and the environment [39]. [SEP] Justify:

Output: The text contrasts the claim by highlighting that while some subgroups with high vegetable intake may meet certain vegetable recommendations, they do not necessarily achieve adequate consumption in other food groups critical to a healthy diet. It emphasizes a whole-of-diet approach rather than focusing exclusively on vegan or plant-based diets, indicating that many patterns of eating, including those low in animal products, may still lack nutritional adequacy. The text suggests that a diet low in animal-based foods does not automatically ensure healthiness, demonstrating that a vegan diet is not inherently healthier than others without proper consideration of a balanced diet.


Input: Task: pass the claim using the given text. Claim: Priming can affect behavior without conscious awareness. [SEP] Text: The goodness of fit was evaluated for these three participant’s data individually for each experiment given these best-fitting values, and was found to be very good for all across two different null models (R2 = 0. 636 ± 0. 172; BIC = 4669. 31 ± 960. 94). However, the single free parameter θ does not provide any significant improvement in the fit of the model to the data: with no free parameters, R2 = 0. 617 ± 0. 141 and BIC = 4657. 10 ± 960. 73. Thus, we present the parameter-free model in the main text. Comparison of model to behavioral data We examined the relative agreement between our model’s predictions and collected behavioral data by calculating the multinomial likelihood of the model given the observed data (Dorfman and Alf, 1969), which has previously been used within a signal detection framework. Formally, the likelihood of a certain model m (with a given set of parameters φ) can be expressed as, (A4-2) Lm(ϕ|data) ∞∏ijPϕ (Ri|Sj)ndata(Ri|Sj) where each Ri is a behavioral response a subject may produce on a given trial, and each Sj is a type of stimulus that might be shown on that trial. The expression ‘ndata(Ri Sj)’ is a count of how many times a subject actually produced Ri after being shown Sj. The expression ‘Pϕ(Ri Sj)’ denotes the probability with which the subject produces the response Ri after being presented with Sj, according to the model specified with parameters ϕ. This corresponds to the percentage of time each of the models described above produced response Ri after having been ‘presented’ with stimulus Sj. Note that this approach does not examine the performance of a model relative to the behavioral data with reference to any summary statistics, but instead allows for fitting of the full distribution of probabilities of each response type contingent on each stimulus type. Given that the model cannot meaningfully represent the physical contrast presented to a human participant, we simulated all possible contrast evidence values on a scale of 0 to 5 in steps of 0. 2, and calculated the percent correct orientation discrimination performance predicted by the model at each of these contrast evidence levels. [SEP] Justify:

Output: The text does not provide any information regarding the concept of priming or its effects on behavior, making it irrelevant to the claim. Instead, it discusses the evaluation of participant data and model fitting for behavioral predictions, without addressing conscious awareness or priming.


Input: Task: corroborate the claim using the given text. Claim: Climate change is causing more extreme weather events worldwide. [SEP] Text: In general, increases in natural disaster media volume overall and especially around specific major natural disasters, highly corresponded to increased climate change media volume, for both tweets and news, with clear positive linear relationships emerging between the two parameters. 5) Direct attribution of elevated climate change media to extreme weather Seventy-eight percent of climate change tweet maxima (above 2 standard deviations from a month mean) corresponded to natural disasters news volume above the 50th percentile of natural disasters news volume (Fig. 5). Similarly, over 50% of climate change tweet volume maxima corresponded with Q4 natural disasters news volume, and 26% with 90th percentile or higher natural disaster news volume. The relationship between these two variables is strong, with a clear negative linear trend (correlation of −0. 992 and R squared of 0. 985), further supporting the notion that climate change discussion tends to spike around natural disasters (indicated by high news volume, which is a less noisy factor than tweet volume). The associated model can be used to extrapolate additional data points from the 50th to 90th percentile of natural disaster news volume alone. Size Fig. 5. Climate change tweet volume local maxima correlation with natural disasters. The relationship between climate change tweet local maxima, defined as being at least 2 standard deviations (SD) above the mean tweet daily average for the specific dates’ month is shown. Standard deviations and means were calculated on a month-to-month basis, as for other analyses. The news volume percentile represents the news volume percentile threshold (calculated on a monthly basis), and the second column represents the proportion of the climate change maxima previously discussed that also have natural disaster news volume above the corresponding percentile in the leftmost column. The linear regression model and scatterplot in the right area of the figure represent the relationship between news volume percentile and the proportion of climate change tweet maximums that fulfill it. Citation: Weather, Climate, and Society 15, 1; 10. 1175/WCAS-D-22-0053. 1Download FigureDownload figure as PowerPoint slide 4. Discussion a. Long-term trends and patterns Public association of climate change with extreme weather disasters, quantified through tweets and other media, effectively doubled between 2010 and 2020. According to a University poll of 300 individuals across 43 states, approximately 85% of Americans are to some degree fearful of extreme weather events (Ransford and Strategist 2014). [SEP] Justify:

Output: The text corroborates the claim by showing a strong correlation between climate change discussions and extreme weather events. It states that 'the relationship between climate change tweet local maxima and natural disasters' indicates that as media volume around climate change increases due to natural disasters, it supports the notion of an association between climate change and extreme weather events. Additionally, it mentions that public association of climate change with extreme weather disasters doubled between 2010 and 2020, reinforcing the idea that climate change is perceived as contributing to more extreme weather events.


Input: Task: contrast the claim using the given text. Claim: A vegan diet is automatically healthier than other diets. [SEP] Text: The PHRD provides a set of food intake targets for a diet that was promoted as healthy for both people and the planet. Health pattern of eating includes more plant-based foods and fewer animal-based foods than currently consumed in Australia (and most developed countries) and as such, almost all the selected Australian diets fell well short of the PHRD recommendations for vegetables, wholegrains, legumes and nuts, and were well above recommendations for dairy foods, red meat, and discretionary foods (some of which contain animal-sourced ingredients). The PHRD modelled in the Australian context provided inadequate amounts of calcium (71% of the EAR). Prevalence of inadequate consumption of calcium in Australia is already high, with 73% of females and 51% of males aged two year and over consuming less than the calcium EAR [43]. Therefore, the PHRD dietary pattern may pose further risks for bone health if adopted. However, the PHRD was the diet with the lowest estimated environmental impacts so among the diets modelled here could be considered the healthiest for the planet. The emphasis on consuming a wide variety of foods within the current ADGs is sometimes overlooked but re-emphasising the statements around promoting a variety of “different types and colours” of vegetables and “the wide variety of foods” within the meat and alternatives group would be important for human and planetary health in contemporary dietary guidance. The average Australian diet does not exceed the recommended number of servings of the meat and alternatives food group set out in the ADGs or the PHRD, but the proportion of animal-based to plant-based servings does not align with either set of guidelines. Australians consume 73% of servings from the meat and alternatives food group as red meat and poultry. Whereas the ADGs suggest about one third of servings from this food group come from red meat and poultry. This ADGs recommendation, therefore, falls between current intakes (73%) and the PHRD (10% of the meat and alternatives food group to be consumed as red meat and poultry). Diversifying food choices within the meats and alternatives food group, and all food groups for that matter, is recommended. However, this would require significant dietary change for most Australians (intake of legumes would need to increase by about 20-fold). Substantially increasing legume consumption would be a big cultural shift for Australians, as they are not commonly part of meals [44]. [SEP] Justify:

Output: The text contrasts the claim by discussing the inadequacies and risks associated with the PHRD dietary pattern, which emphasizes plant-based foods but provides insufficient calcium, potentially harming bone health. It specifically states that the PHRD diet may pose further risks compared to the average Australian diet, and it highlights the need for a variety of food choices rather than solely adopting a vegan diet for health benefits. This suggests that a vegan diet is not inherently healthier than other diets, as it may lack critical nutrients like calcium.


Input: Task: pass the claim using the given text. Claim: Priming can affect behavior without conscious awareness. [SEP] Text: We then matched each participant’s data to the closest ‘contrast evidence’ level in the model by identifying ‘contrast’ levels at which each model performed similarly to the participant on the orientation discrimination task, i. e. matching the percent correct orientation discrimination performance between the model and the data for each contrast level presented to a human participant. The remainder of the summary statistics used in the analyses -- percent of time TP interval was chosen, Type 2 Rate, and percent correct orientation discrimination conditional on TP chosen or unchosen -- can then all be derived from this full behavioral profile of stimulus-contingent response probabilities, allowing for comprehensive comparison between model predictions and behavioral data. We used this measure both to explore the goodness of fit (see below) of the model given no free parameters, and also to evaluate the ideal observer’s dependence on the free parameter θ to the observed behavioral data in all experiments on a subject-by-subject basis, by maximizing the likelihood of the model parameters given the data (Equation A4-2) for each model-subject combination. To compare between two models with unequal numbers of parameters, it is standard to use the Bayesian information criterion (BIC), which penalizes more complicated models according to the number of free parameters to avoid overfitting. The BIC is calculated as (A4-3) BIC = -2·lnLm∅data + k·ln(n) where Lm∅datais defined as in Equation A4-2, k is the number of free parameters, and n is the number of data points. Maximum likelihood fits were accomplished using a customized Nelder-Mead simplex search algorithm (Lagarias et al. , 1998) to minimize the negative log-likelihood (Equation A4-2). Model predictions were accomplished through simulation, with 10,000 paired-interval ‘trials’ at each simulated stimulus contrast level. Goodness of fit To quantify the goodness of fit for the parameter-free ideal observer model presented in the main text, as well as for the model utilizing the best-fitting value of θ for each participant, we calculated the generalized coefficient of determination, R2, as described in (Nagelkerke, 1991). [SEP] Justify:

Output: The text does not address the claim regarding priming and its effects on behavior. It discusses details about experimental methodology, model comparisons, and statistical measures, which are unrelated to the claim about priming and conscious awareness.


Input: Task: corroborate the claim using the given text. Claim: Climate change is causing more extreme weather events worldwide. [SEP] Text: Thus, given Americans’ higher propensity for extreme weather fear than climate change fear (85% vs 60%), public correlation between the two goes a long way toward improving environmental awareness and desire to act on climate change. Furthermore, public belief in climate change undoubtedly grew between 2010 and 2020, likely because of the success of increased environmental awareness messaging. This also contributed toward a developing U. S. political landscape surrounding the importance of environmental issues. For example, in 2020, 60% of Americans were shown to be seriously worried about climate change, while that same metric was only 32% in 2010 (Kohut et al. 2010; Poushter and Fagan 2020). As a result of this increase in concern, 65% of Americans now believe the government is doing too little to combat climate change—a direct and measurable shift in attitude (Tyson and Kennedy 2020). In accordance with long-term growing public association trends, this study found strong evidence to suggest that individual extreme weather events often directly relate to sizable increases in climate change tweet and news volume, a trend exacerbated for disasters that sparked greater general media conversation, as would be expected. Thus, natural disasters, especially prominent ones, clearly encourage more climate change discussion. Similarly, around specific extreme weather events, the number of dual-category (climate change and natural disaster) tweets and news 5. disasters and their corresponding media discussion led to evident increases in climate change tweet and news 1. Detailed yearly volume and sentiment basic overview metrics. Table A2. Detailed time-interval volume overview metrics. Table A3. Detailed overview metrics on NOAA dataset natural disasters. Costs are in millions of U. S. dollars. 2017. J. Extreme Events, 6, 1950002, W. , and P. Berglez, 2019: How do social media users link different types of extreme events to climate change? A study of Twitter during 2008–2017. J. Extreme Events, 6, 1950002, falseSearch 10. 1111/j. 1751-9020. 2008. 00188. x. Anderson, A. , 2009: Media, politics and climate change: Towards a new research agenda. Sociol. Compass, 3, 166–182, falseSearch 2017. Environ. Hazards, 20, 382–399, P. , and W. Al-Saqaf, 2021: Extreme weather and climate change: Social media results, 2008–2017. [SEP] Justify:

Output: The text corroborates the claim by indicating that there is a public correlation between extreme weather events and climate change, which suggests that individual extreme weather events often lead to increased discussion about climate change. It highlights that as concern over climate change grows among Americans, there is a corresponding increase in attention to extreme weather events. Specifically, the mention of 'strong evidence to suggest that individual extreme weather events often directly relate to sizable increases in climate change tweet and news volume' supports the idea that climate change is linked to more frequent extreme weather events, aligning with the claim.


Input: Task: contrast the claim using the given text. Claim: A vegan diet is automatically healthier than other diets. [SEP] Text: Reductions in animal meats can make it more difficult to achieve adequate iron and zinc intakes so careful meal planning is required, particularly as the bioavailability of these nutrients is lower in plant-based foods. Further, switching to some highly processed plant-based meat alternatives may not have the assumed health benefits [45,46,47], and other plant-based alternatives such as nuts may also come at a higher environmental cost depending on local environmental constraints [35]. Therefore, developing dietary advice can be difficult as there are trade-offs between food, health and the environment that need consideration. Consistent across all of the selected Australian diets examined was a pattern of eating that exceeded both the global and national recommendations for discretionary foods and beverages, which are those energy dense, nutrient poor items high in sodium, added sugar, and saturated fat, that when consumed in excessive amount can increase the risk of weight gain and obesity [48, 49]. The average Australian diet contained about twice the national recommended maximum intake for discretionary foods and beverages. The widespread overconsumption of discretionary foods means it is increasingly important to examine their impacts on the healthiness of diets, and in turn their environmental impacts [50]. Excessive consumption of discretionary choices leaves insufficient room in the diet for the five healthy food group foods. Evidence is emerging around the health implications of overconsuming ultra-processed foods [48, 51] and evidence is also starting to emerge around the environmental impacts of discretionary, ultra-processed foods and beverages. Discretionary foods and beverages have been estimated to account for 28–34% of diet-related climate [34], water [35], cropland [36] and pesticide [52] footprints of the Australian diets. It is also important to consider the opportunity cost of processing and consuming these foods. Dietary guidelines suggest they are not a necessary part of the diet as they may displace healthier foods, but they do have a place in a healthy diet for variety and enjoyment [26], so again finding the balance around enjoyment, health and environmental sustainability is important. This study used data from a nationally representative sample of Australians through which dietary intake was assessed using a robust 24-h recall methodology and the sampling framework of the survey allowed for inferences about the population. However, dietary survey data using 24-h recall are likely to under report true dietary intake based on analysis of energy requirements [21, 53], and misreporting may occur differentially between different food groups, which may have influenced results. [SEP] Justify:

Output: The text contrasts the claim by highlighting that a vegan diet may not be healthier without careful meal planning, as reductions in animal products can lead to inadequate iron and zinc intakes. It notes that processed plant-based alternatives may not provide the expected health benefits and emphasizes the importance of balancing dietary choices to achieve health and environmental objectives. Additionally, it points out risks associated with overconsumption of discretionary and ultra-processed foods, which can diminish diet quality regardless of the diet type.


Input: Task: pass the claim using the given text. Claim: Priming can affect behavior without conscious awareness. [SEP] Text: This provides a continuous measure ranging from zero (corresponding to worst fit) to one (corresponding to best fit) based on the maximum likelihood criteria of fit. The formula used is (A4-4) R2 = 1-exp-2nlβ^-l0 where n is the number of data points, and lβ^ and l0 denote the log-likelihoods of the fitted and the null model, respectively. We explored two null models: (1) A model which produces every response type with equal probability (i. e. , randomly guesses); and (2) a model which knows the correct answer with 100% probability. The generalized R2 is interpreted as the proportion of variance in the data that is explained by the model. Biological plausibility We also considered several other modifications to make our model more biologically plausible. Multiplicative noise is considered due to the observation that neurons often display constant or near-constant Fano factor F, where F = σ2WμW, where σ2W is the variance and μW the mean of the neuron’s firing rates in some time window W. In other words, the variance increases as mean firing increases, leading to increased neuronal variability with stronger stimulus inputs. Additionally, both multiplicative and additive noise are considered elements of the noise in neural firing rates and behavioral responses (e. g. , Dosher and Lu, 1998). We thus considered two additional free parameters, individually and in conjunction with one another: 1. f - signal-dependent (multiplicative) noise, or correction for near-constant Fano factor: the variance-covariance matrix is modified to be ∑ = cf00cf· 2. ε - additive noise: the variance-covariance matrix is modified to be ∑ = ε00ε Notably, neither of these parameters -- individually or in conjunction with one another -- produced any significant effect on qualitative model trends or fit (Equation A4-2,A4-3). 10. 7554/eLife. 09651. 019 Once the most likely values for c^leftand c^right have been obtained, the observer refers to these new ‘best guess’ source distributions as S^c^leftand S^c^right (0. 5. [SEP] Justify:

Output: The text is irrelevant to the claim as it discusses statistical modeling, noise in neural firing rates, and biological plausibility without mentioning priming or its effects on behavior. It does not provide any information related to the concept of priming and its potential impact on behavior unconsciously.


Input: Task: corroborate the claim using the given text. Claim: Climate change is causing more extreme weather events worldwide. [SEP] Text: We will use an idealized risk-based approach motivated by the analysis of Hansen et al. (2012) of summer-season temperature extremes over land. They found the normalized and spatially aggregated distribution was approximately normal and exhibited a gradual, decade-by-decade, shift to warmer values relative to the 1951–80 reference period, as illustrated in Fig. 2. A variety of practical details must be considered when analyzing actual data to obtain plots like Fig. 2 (Rhines and Huybers 2013; Sippel et al. 2015). For the purposes of this discussion we will assume we have arrived at an accurate representation of the probability distribution for the daily maximum temperatures in a given month in the natural world without anthropogenic influences (the control climate), and a second distribution representing corresponding maximum temperatures in the current climate. For simplicity, both sets of temperature observations are assumed to be normally distributed with the same standard deviation σ. The mean daily maximum temperature in the warmer world is assumed to have shifted toward warmer temperatures by σ/2 from the mean in the control climate μc. An extreme warm event will be defined as a daily maximum temperature that is more than 2σ warmer than μc, or equivalently, the warmest 2. 3% of the climatological distribution of the daily maximums for the given month in the control climate. According to this definition, an extreme daily maximum temperature in a given month in the control climate would occur, on average, about four times in a 6-yr period. Size Fig. 2. Frequency of occurrence of local June–August temperature anomalies (relative to the 1951–80 mean) for land in units of local standard deviation (horizontal axis). Temperature anomalies in 1951–80 closely match the normal distribution (green curve), which is used to define cold (blue), typical (white), and hot (red) seasons, each with probability 33. 3%. Also indicated is the area greater than 3σ (deep red). Computations use the same set of 1,886 stations that were reporting over the entire period. Adapted from Hansen et al. (2013). Citation: Bulletin of the Society 101, 8; 10. 1175/BAMS-D-20-0026. [SEP] Justify:

Output: The text corroborates the claim by discussing the analysis of temperature extremes and their shifts towards warmer values due to climate change. It details a model involving a control climate without anthropogenic influences and a current climate that shows an increase in daily maximum temperatures, which can be associated with an increase in extreme weather events. The definition of extreme warm events and the frequency of occurrence as related to climate change supports the idea that climate change is contributing to more extreme weather.


Input: Task: contrast the claim using the given text. Claim: A vegan diet is automatically healthier than other diets. [SEP] Text: Change in diet can help to build demand for healthy and more sustainably produced food, but the production and manufacturing of food and the amount of food that is wasted or lost during production can also be important points of intervention as these too can have a positive impact on human health and environmental sustainability of the entire food system [34, 58]. ConclusionsGlobal dietary guidelines may well influence the revision of national dietary guidelines, but a country’s dietary guidelines are designed to better support dietary change as they are more considerate of local culture and practices. Local guidelines are therefore thought to be more achievable and feasible for adoption by the intended population, however in this analysis the average Australian diet fell short of many recommendations in the ADGs. Some of the messages contained within the current ADGs are consistent with the recommendations of Diet, but some differ considerably. Regardless of the set of guidelines referred as the benchmark, Australian diets contained suboptimal amounts of foods from the five food groups, including vegetables, wholegrains, and plant-based protein sources and they exceed the maximum recommended intake of discretionary foods and beverages. Significant behaviour change is required to improve the healthiness and environmental sustainability of population dietary habits in Australia, and these results provide a deeper understanding of areas that are furthest from recommendations. Availability of data and materials The dietary intake data are publicly available from the of Statistics ( accessed on 15 March 2017). The additional dataset used for this analysis are available from the corresponding author on reasonable request. AbbreviationsADGs: : : Department of Agriculture 2019. FAO. Food-based dietary guidelines 2022 [Available from: Dietary strategies to reduce environmental impact: a critical review of the evidence base. Adv Nutr. 2017;8(6):933–46. 83. 2016. The global syndemic of obesity, undernutrition, and climate change: the Lancet commission report. The lancet. 2019;393(10173):791–846. 92. 3. Ministry of Food AaFoD. Dietary Guidelines- good for health and climate. In: editor. Denmark: Veterinary and Food Administration; 2021. Ministry of Health of Brazil. for the Brazilian population. In: Ministry of Health of Brazil, Secretariat of editors. Brazil: Ministry of Health of Brazil; 2015. Council of Health. Guidelines. In: Department PH, editor. Doha, Qatar: Council of Health; 2015. [SEP] Justify:

Output: The text contrasts the claim by indicating that the average Australian diet falls short of dietary recommendations, which implies that simply adhering to a vegan diet does not guarantee a healthier outcome. Additionally, it emphasizes that dietary guidelines should accommodate local cultures and practices, suggesting that a one-size-fits-all approach, such as advocating for veganism as the healthiest diet, may not be appropriate for everyone. This presents the idea that healthiness in diets is relative and dependent on various factors, including local dietary habits and not solely defined by being vegan.


Input: Task: pass the claim using the given text. Claim: Priming can affect behavior without conscious awareness. [SEP] Text: Top bar navigation Frontiers in About us About us Who we are and progress Frontiers' impact2022All progress reportsPublishing model How we publishOpen accessFee policyPeer reviewResearch integrityResearch TopicsServices SocietiesNational consortiaInstitutional partnershipsCollaboratorsMore from ForumPress officeCarbon footprintCareer opportunitiesContact us All journalsAll 10. 3389/fnhum. 2013. 00891 Phenomenal awareness can emerge without attention Jaan Aru1,2*Talis Bachmann2 1Department Neurophysiology, Max-for Brain Research, Frankfurt, Germany 2Department of Criminal Law, Criminology and Cognitive Psychology, Laboratory of Cognitive Neuroscience, Institute of Public Law, University of Tartu, Tallinn, commentary onThe attentional requirements of consciousness by Cohen, M. A. , Cavanagh, P. , Chun, M. M. , and Nakayama, K. (2012a). Trends Cogn. Sci. 16, 411–417. doi: 10. 1016/j. tics. 2012. 06. 013 Response to Tsuchiya et al. : considering endogenous and exogenous attention. by Cohen, M. A. , Cavanagh, P. , Chun, M. M. , and Nakayama, K. (2012b). Trends Cogn. Sci. 16, 528. doi: 10. 1016/j. tics. 2012. 09. 002 Top-down attention and consciousness: comment on Cohen et al. by Tsuchiya, N. , Block, N. , and Koch, C. (2012). Trends Cogn. Sci. 16, 527. doi: 10. 1016/j. tics. 2012. 09. 004 In a recent debate, the views that top-down attention is necessary for consciousness (Cohen et al. , 2012a,b) and that consciousness is independent of top-down attention (Tsuchiya et al. , 2012) have clashed. Here, we list the overlooked or ignored arguments that should be considered before deciding that consciousness is inevitably the result of attention. The issue of relation of bottom-up attention and consciousness seems to have two possible solutions according to the evidence available at present. First, based on the current evidence it may be agreed upon that conscious experience cannot be dissociated from bottom-up exogenous attention (Tsuchiya et al. , 2012). [SEP] Justify:

Output: The text does not address the concept of priming or its effects on behavior or consciousness. Instead, it discusses the relationship between attention and consciousness, presenting different views on whether top-down attention is necessary for consciousness. Therefore, it is irrelevant to the claim regarding priming.


Input: Task: corroborate the claim using the given text. Claim: Climate change is causing more extreme weather events worldwide. [SEP] Text: The curves show the value for normal distributions where the mean has warmed relative to the control climate by σ/2 (blue), σ (orange), and 3σ/2 (green). Points plotted as symbols are the 2019-averaged FAR and POD metrics for NWS warnings for high wind (HW), severe thunderstorms (ST), winter storms (WS), and flash floods (FF). T1 and T2 plot the average () values from Fig. 1 for tornado warnings from 2003 to 2011, and 2014 to 2017, respectively. The () values for the cases illustrated in Figs. 3 and 4 are indicated by blue dots. Citation: Bulletin of the Society 101, 8; 10. 1175/BAMS-D-20-0026. 1Download FigureDownload figure as PowerPoint slideDiscussion and conclusionsWhen it is possible to determine probability distributions for the occurrence of some extreme event in the current climate and in a control climate uninfluenced by anthropogenic forcing, those two distributions themselves contain the most complete information about changes in the occurrence of the event in the warmer world. Using methods adapted from epidemiology (Stone and Allen 2005), this information can be distilled into important measures of the warming-induced changes in the probability of events exceeding a given threshold, such as the risk ratio and the fraction of attributable risk: measures that have proved very useful in previous attribution studies. Here we have considered how the same information about shifts in the probability distribution of some event under climate change might be used in connection with attribution rubrics whose expected average performance can be assessed using familiar weather-forecast-verification metrics like the probability of detection and the false alarm ratio. In our maximum-temperature example, we considered a modest one-half standard-deviation shift in the mean from the control to the current climate. As shown in Fig. 6, when larger shifts occur, the false alarm ratios are substantially reduced, and the contributions from climate change can become rather clear cut. If the shift in the mean is a full standard deviation, as suggested by the rightmost panel in Fig. 2, the FAR and the probability of a type I error arising from the seemingly incautious strategy of attributing all temperatures greater than μc + 2σ to global warming decreases to just 0. 14. The probability of detection in this case is a perfect 1. [SEP] Justify:

Output: The text corroborates the claim by discussing how changes in climate, specifically warming, can alter the probability distributions of extreme weather events. It indicates that the information about shifts in these distributions in a warmer world contains crucial evidence linking climate change to increasing occurrences of events exceeding certain thresholds, which would include extreme weather events. The reference to measures like risk ratios and fractions of attributable risk indicates an established relationship between climate change and the frequency or intensity of extreme weather events.


Input: Task: contrast the claim using the given text. Claim: Detox diets are necessary to cleanse the body of toxins. [SEP] Text: Twelve healthy normal-weight men switched from their habitual diet (48% carbohydrate) to a ketogenic diet (12% carbohydrate) for 6 weeks, and 8 men served as controls consuming their normal diet. Fat mass, assessed by dual energy x-ray absorptiometry, was significantly decreased (−3. 4 kg) and lean body mass significantly increased (1. 1 kg) after the ketogenic diet. There was a significant decrease in serum insulin (−34%), and 70% of the variability in fat loss on the ketogenic diet was accounted for by the decrease in serum insulin concentrations. In addition to experimental demonstrations of its efficacy, the importance of carbohydrate restriction rests on the fundamental idea that carbohydrate is more than an energy source. In distinction to strategies based on reduction in dietary fat, the rationale for reduction in dietary carbohydrate derives from basic mechanisms. Carbohydrate is the major stimulus of insulin and, beyond its role in providing a source of energy, serves as a control element, either directly via glucose or fructose or indirectly through the effects of insulin and other hormones. It is difficult to attribute metabolic responses and clinical outcomes to one class of nutrients, but as a principle, one has to consider the inextricable link between dietary carbohydrate and the appearance of plasma glucose and insulin as an important modulator of cellular function. manifold functions of insulin can be summarized as anabolic. Insulin inhibits breakdown and promotes storage of nutrients. In this way, dietary carbohydrate-induced increases in circulating glucose and insulin levels serve as an important control element on metabolism, especially the regulation of fuel selection between carbohydrate and fat. Carbohydrate restriction stimulates a unique metabolic state characterized by increased fat oxidation and decreased fat synthesis. In fact, adipose tissue lipolysis is exquisitely sensitive to changes in insulin within the physiological range of concentrations (6) (Figure 1). Small to moderate decreases in insulin can increase lipolysis several-fold, the response being virtually immediate. Insulin also stimulates lipogenesis by increasing glucose uptake and activating lipogenic and glycolytic enzymes. Small reductions in insulin levels, such as that easily achieved with dietary carbohydrate restriction, remove the normal inhibition on fat breakdown. Thus, low-carbohydrate diets are associated with significant changes in lipid metabolism, favoring decreased storage and increased breakdown and oxidation of fat as well as improvement in atherogenic dyslipidemia. [SEP] Justify:

Output: The text contrasts the claim by focusing on the metabolic effects of a ketogenic diet, emphasizing the physiological roles of carbohydrates and insulin rather than supporting the idea of detox diets as necessary for cleansing the body. It describes how carbohydrate restriction can influence metabolic processes and facilitates fat oxidation but does not mention the need for detoxification from toxins.


Input: Task: pass the claim using the given text. Claim: Priming can affect behavior without conscious awareness. [SEP] Text: A. F. (2010). How neuroscience will change our view on consciousness. Cogn. Neurosci. 1, 204–220. doi: 10. 1080/17588921003731586 | Text |. (2001). Effects of voluntary attention on structured afterimages. Perception 30, 1439–1448. doi: 10. 1068/p3127 | Text |. , Luiga, I. , Kreegipuu, K. , and Bachmann, T. (2010). Scotomas induced by multiple, spatially invariant TMS pulses have stable size and subjective contrast. Int. J. Psychophysiol. 77, 157–165. doi: 10. 1016/j. ijpsycho. 2010. 06. 001 | Text |. (2012). Hallucinations. London: Pan Macmillian. Tsuchiya, N. , Block, N. , and Koch, C. (2012). Top-down attention and consciousness: comment on Cohen et al. Trends Cogn. Sci. 16, 527. doi: 10. 1016/j. tics. 2012. 09. 004 | Text | CrossText van Boxtel, J. J. , Tsuchiya, N. , and Koch, C. (2010). Opposing effects of attention and consciousness on afterimages. Proc. Natl. Acad. Sci. U. S. A. 107, 8883–8888. doi: 10. 1073/pnas. 0913292107 | Text |. and Lamme, V. A. F. (2012) Non-attended representations are perceptual rather than unconscious in nature. 7:e50042. doi: 10. 1371/journal. pone. 0050042 | Text |. , Holzmann, R. , Tuin, I. , and Hobson, J. A. (2009). Lucid dreaming: a state of consciousness with features of both waking and non-lucid dreaming. Sleep 32, 1191–1200. | Text Keywords: attention, consciousness, awareness, afterimages, dreams, transcranial magnetic stimulation, phenomenal consciousness Citation: (2013) Phenomenal awareness can emerge without attention. Front. Hum. Neurosci. 7:891. doi: 10. 3389/fnhum. 2013. [SEP] Justify:

Output: The text does not address the claim directly. It lists various research studies and authors that pertain to consciousness and attention but does not provide any specific support or contradiction regarding the claim that 'priming can affect behavior without conscious awareness'. Thus, it is irrelevant to the claim.


Input: Task: corroborate the claim using the given text. Claim: Climate change is causing more extreme weather events worldwide. [SEP] Text: : 2157-7617 Journal of & -in-for Manuscript 10. 4172/2157-7617. 1000173 Events and Impact on (SME's): Responses for Sustainability of SME's Shubham Gandhi*, Department of *Corresponding Author: Shubham Gandhi, Department of -110042, Delhi, India, Tel: 011-27871018, Email: shubhamgandhi14@gmail. com Received: 29-Jul-2013 Accepted Date: 28-Nov-2013 Published Date: 05-Dec-2013 DOI: 10. 4172/2157-7617. 1000173 Abstract Purpose: India has a rich tradition of cultural practices and a textual heritage that dates back to several hundreds of years. The intellectual achievements of Indian thought are found across several fields of study in ancient Indian texts ranging from the Vedas and the Upanishads to a whole range of scriptural, philosophical, scientific, technical and artistic sources, which advocates environmentalism. However as the globalization took roots in India in late 80's and to withstand the competition face from global markets ignored the environmental safety and as results of this pollution is now crossing critical limits in metro cities. Delhi experienced a number of Events (EWEs) during recent years and a significant number of businesses were affected including the construction sector, which is a major employment driver, being the second largest employer in the country, next only to agriculture. However, little research work has been undertaken on how construction industry is at the risk due to the EWEs. The paper profoundly aims at exploring various such EMEs and their impact on the construction businesses. And finally suggests the ways through which it could be tackled. Research design/methodology/approach: A mix and match research design was adopted to elicit information from construction industry, involving a questionnaire survey and interviews to obtain responses from construction practitioners. Mean was used to establish the average scores for analysis and conclusion. Direct and Indirect impacts from extreme weather events on logistics, people premises, process, markets and finance are discussed in detail. Findings/conclusions: Direct and indirect impacts from climate change include damage to products and tools from higher temperatures and increased rainfall, with consequent delays and increased costs to projects. The predominance of outdoor workers in this sector leaves its employees at higher risk from increased direct-heat stress, mostly dehydration all leading to severe effect on people. [SEP] Justify:

Output: The text corroborates the claim by discussing the direct and indirect impacts of climate change, specifically mentioning how higher temperatures and increased rainfall due to climate change affect businesses in the construction sector. It highlights that Delhi has experienced numerous extreme weather events (EWEs), which are influenced by climate change and significantly impact economic activities, thus supporting the claim that climate change is causing more extreme weather events globally.


Input: Task: pass the claim using the given text. Claim: Detox diets are necessary to cleanse the body of toxins. [SEP] Text: On the 5th day, the pups were separated, matched according to litter and sex, and randomized to one of the four groups studied. Dam-reared litters were culled to 10-12 pups per dam. Artificial rearing procedure. A method of AR described by Diazet al. (25) was modified for our use. Five-day-old neonatal Sprague-Dawley rat pups underwent AR until 19 d of age. Pups randomized to AR underwent a gastrostomy procedure as described by Westet al. (35) with inhaled isofluorane anesthesia. AR pups were then fed by intermittent infusion of RMS by an automated syringe pump. Feedings were given as 10-min infusions each hour over a 24-h period. The total volume of formula given in milliliters was one-third the pup weight in grams, updated daily. Many of the procedures used in AR and for the manipulation of the fatty acid content of RMS have been previously described by Ward et al. (36). A percutaneous gastrostomy was placed, and artificial rearing was accomplished according to the method of Messer as modified by Hall(37) and Diaz et al. (25). The RMS was delivered to each rat pup in the following manner. A 12-mL syringe(Monoject no. 8881-512910, Sherwood Medical, St. Louis, MO) containing the specified daily volume of RMS was placed on a multisyringe infusion pump (no. 55-4899, Harvard apparatus, Southnatick, Mass). The pump was cycled with an electronic timer (XT-4, Corp. , San Diego, CA). The syringe was capped with a 22-gauge syringe needle (no. 305155, Becton Dickinson, Parsippany, NJ) to which a 3-ft length of polyethylene tubing (0. 58 mm inside diameter, PE-50, Becton Dickinson) was attached. The other end of the 3-ft long tube was connected to 0. 28-mm inside diameter, 0. 61-mm outside diameter (PE-10, Becton Dickinson) gastrostomy tubing (10 cm in length) implanted in the rat pup. Between d 5 and 12 the AR rat pups were housed in round plastic containers with hardwood chip bedding. [SEP] Justify:

Output: The text provided is irrelevant to the claim regarding detox diets and their necessity for cleansing toxins from the body. It discusses a study involving artificial rearing of neonatal rat pups and their feeding methods, without mentioning detox diets, toxins, or any related concepts. Thus, it does not support or contrast the claim.


Input: Task: pass the claim using the given text. Claim: Attention can be selectively focused, altering cognitive processing. [SEP] Text: Participants were provided informed consent before completing all study demographics and questionnaires. Participants were seated approximately 70 cm from the stimulus presentation monitor and EEG electrodes were attached. Participants then completed a cued picture viewing task. After completion of the study’s procedures, participants were compensated ($20) for their time. Cued picture viewing taskA modified version of the S1-S2 tasks used in previous research was used for cued picture viewing (Grant et al. , 2015; Fig. 1). Participants were instructed that they would see a series of pictures and that, prior to each picture (S2), they would see a colored (either yellow or blue) “+” cue (S1) that indicated a picture was about to be presented. The color of the S1, which was counterbalanced across participants, was paired with the valence of S2, although participants were uninstructed regarding the color-valence pairing (e. g. , they were not informed that a yellow cue indicated a subsequent neutrally valanced picture). In addition to the color indicating the valence of the upcoming picture, the S1 stimuli also were manipulated to enhance self-focused attention. The S1 cue was presented in two orientations (0° or 45° rotation) in a 50:50 ratio. Participants were told that one of the orientations would appear when their heart rate increased (Judah et al. , 2016). As in prior protocols (Judah et al. , 2016), to increase believability, participants were told that this was caused by a software glitch and that they could ignore it as it was not relevant to the task. The orientation was randomized across the task, and the 0°/45° rotation-HR pairing was counterbalanced across participants. The order of stimuli presentation within subject was also randomized. Participants viewed the S1 stimulus for 500 ms, followed by a 3,500-ms interstimulus interval, and the S2 stimulus for 2,000 ms. Fig. [SEP] Justify:

Output: The text is irrelevant to the claim as it primarily describes the methodology of a study involving cued picture viewing and participant instructions without addressing the concept of selective attention or its effects on cognitive processing.


Input: Task: corroborate the claim using the given text. Claim: Climate change is causing more extreme weather events worldwide. [SEP] Text: • Small and medium contractors do not have the wherewithal to upgrade their capability. • Quality, safety, environment and social aspects are also not being addressed appropriately. Also, Due to the nature of their operations, businesses operating in the construction sector have always been considered significantly vulnerable to weather extremes. Indeed, Mills [1] identified the construction sector as one of the most vulnerable to climate change and EWEs. Therefore there is a need to increase the understanding the risk associated with it. And thus greater research has to be undertaken on how construction SMEs responds to the risk of EWEs and build resilience. EWE and construction industry Basically, the climate of India is dominated by the summer monsoon (June to September). The entire year is, however, divided into four seasons: (i) Winter (December and January) (ii) Pre-monsoon or season (March-May) (iii) Southwest or season (June - September) (iv) Post monsoon season (October - November). Year to year deviations in the weather and occurrence of climatic anomalies extremes in respect of these four seasons are: • Cold wave, Fog, Snow storms • Hailstorm, Thunderstorm and Dust storms • Heat wave • Floods, Heavy rain These are all related to the meteorological factors. Whereas earthquakes, volcanoes and tsunamis are other geo physically triggered disasters and not discussed in the present research paper. EWEs can have both direct and indirect effects on the construction sector and those associated with it. Direct effects include disruption to site works as a consequence of the extreme weather event itself (e. g. the site might be flooded); indirect effects include disruption to site works as a consequence of the secondary effects of an extreme weather event (e. g. disruptions to deliveries and utility supplies through the supply chain) [2]. Many authors have found that: there is a lack of forward planning; lack of capital for recovery; ineffectual interactions with national agencies; infra-structure problems [3]; individual attitudes and organizational culture; access to expertise; perceived exposure to risk [4]; and the relatively low importance assigned to climate change and EWEs by the construction sector [5], all contribute to a general inertia amongst organizations to consider resilience to EWEs. Adopted for methodology adopted in this research work is given below • Study of literature related to impact of extreme weather events in construction business. • Preparation of Questionnaire. [SEP] Justify:

Output: The text corroborates the claim by discussing the vulnerability of the construction sector to extreme weather events (EWEs), which are influenced by climate change. It mentions that businesses in the construction sector are significantly affected by weather extremes, thereby supporting the assertion that climate change is leading to more frequent and impactful extreme weather events.


Input: Task: pass the claim using the given text. Claim: Unemployment rates can impact consumer spending and economic growth. [SEP] Text: in through your institution issue search term - content in A - 1 - Browse content in A1 - A10 - General A12 - Relation of Economics to A13 - Relation of Economics to A14 - Sociology of Economics A2 - and Teaching of content in A2 - and Teaching of Economics A29 - - History of Economic Thought, Methodology, and Browse content in B - History of Economic Thought, Methodology, and B0 - General B1 - History of through 1925 Browse content in B1 - History of through 1925 B11 - Preclassical (Ancient, Medieval, Mercantilist, Physiocratic) B12 - Classical (includes Adam Smith) B2 - History of since 1925 Browse content in B2 - History of since 1925 B20 - General B21 - Microeconomics B22 - Macroeconomics B25 - Historical; Institutional; Evolutionary; Austrian B26 - B3 - History of Economic Thought: content in B3 - History of Economic Thought: Individuals B31 - Individuals B4 - Browse content in B4 - B41 - B5 - content in B5 - Approaches B55 - C - Mathematical and Browse content in C - Mathematical and C0 - content in C0 - General C00 - General C02 - C1 - Econometric and and Methodology: content in C1 - Econometric and and Methodology: General C10 - General C11 - Bayesian Analysis: General C12 - Hypothesis Testing: General C13 - Estimation: General C14 - Semiparametric and Nonparametric Methods: General C15 - Methods: General C19 - Other C2 - Models; Browse content in C2 - Models; C21 - Cross-Sectional Models; Spatial Models; Models; C22 - Time-Series Models; Regressions; Effect Models; C23 - Models; Spatio-temporal Models C24 - Truncated and Censored Models; Models; Models C25 - Models; Discrete Regressors; Proportions; Probabilities C26 - (IV) Estimation C3 - Multiple or Models; Browse content in C3 - Multiple or Models; C31 - Cross-Sectional Models; Spatial Models; Models; Quantile Regressions; Models C32 - Time-Series Models; Regressions; Effect Models; Diffusion Processes; Models C [SEP] Justify:

Output: The text does not address the claim regarding the relationship between unemployment rates and consumer spending or economic growth. Instead, it appears to list various topics related to economics and does not provide any relevant information or evidence that would support or contrast the claim.


Input: Task: pass the claim using the given text. Claim: Attention can be selectively focused, altering cognitive processing. [SEP] Text: 1S1S2 taskFull size imageElectrophysiological measuresElectroencephalographic (EEG) data were recorded using a 32-channel (Fp1/2, AF3/4, F7/8, F3/4, FZ, FC5/6, FC1/2, T7/8, C3/4, CZ, CP5/6, CP1/2, P7/8, P3/4, PZ, PO3/4, O1/2, and OZ) BioII system (BioSemi, Amsterdam, The Netherlands) and were sampled at 256 Hz. Two electrodes (Sensor active and Leg passive) were incorporated into the electrode cap and used as ground electrodes. Electrodes were placed on the left and right mastoids and were used for 2021. 0 (Delorme & Makeig, 2004) and version 8. 10 (Lopez-Calderon & Luck, 2014) were used to process the data. Offline, data were re-referenced to the average of the left and right mastoids (Luck, 2014) and filtered using a band-pass filter (0. 01–30 Hz) with a half-amplitude cutoff and a 12 dB/oct roll-off and a 60-Hz notch filter. Ocular artifacts (e. g. , blinks, saccades) were isolated and corrected by using independent components analysis (ICA; Luck, 2014) with manual identification of components with spatial and temporal characteristic of ocular artifacts consistent with those reported in previous research (Mognon et al. , 2011). Automated routines in were used to detect and reject large (200 μV) voltage changes at any electrode in a 200-ms window moving at 50-ms steps. Previous research informed a priori measurement sites and time windows for ERPs and was confirmed with visual inspection. The SPN was measured from the FZ electrode in the 200 ms before the onset of S2 (& Böcker, 2004) and baselined using the 200 ms before the onset of S1. The LPP was measured at PZ 400-1,000 ms after S2 onset (Cuthbert et al. , 2000) and baselined 200 ms prior to S2 onset. Less than 25% of trials were rejected for any subject. The average number of rejected trials across all conditions was 42. [SEP] Justify:

Output: The text is irrelevant to the claim as it primarily discusses the technical aspects of electrophysiological measures and experimental procedures without addressing the concept of selective attention or its effects on cognitive processing.


Input: Task: corroborate the claim using the given text. Claim: Climate change is causing more extreme weather events worldwide. [SEP] Text: • Site visit to major construction project sites. • Questionnaire survey and personnel interviews with in-charges and managers and collection of data from site. Questionnaire design The six identified impact areas and 29 factors formed a questionnaire used as survey tool. Respondents were asked to express the degree of impact EWE is likely to have on their businesses on a five-point Likert scale. After interviewing professionals and different academicians the questionnaire was finalized and was distributed among 60 clients. While interviewing the professionals they were asked to quote their responses based on their experiences in the past. No incentives to participation in the survey were offered. Questionnaires were posted in the months of January-February, 2013. Out of 60 questionnaires only 20 fully filled questionnaires were received i. e. a response rate of 30%. The mean score (out of 5) of each factor under a given parameter was calculated indicating the relative importance of each factor. Significance of the score 1 - Impact, 2 - Low Impact, 3 - Moderate Impact, 4 - and 5 - impact. A descriptive figure of research methodology has been depicted in the flow chart below (Figure 4). Figure 4: A descriptive figure of research methodology. Results and entire respondent said that they had been affected by extreme weather events in the last 2 years. By far the most common extreme weather event recalled by the sample was (4. 517), Rainfall (4. 312) and Drought/Water restrictions (4. 116) followed by low temperature (3. 47) and winds (2. 34) which is not surprising given the fact that the India has been affected by a number of Events (EWEs) in the recent past (Table 1). The 2003, 2012 heat waves, 2005 flood, and 2004, 2005 heavy winter, 2013 drought, 1988 hailstorm are some examples. Such weather extremes have lead to significant economic as well as societal costs. 3. 472 Rain/ flooding 4. 312 Winds 2. 347 4. 517 Drought Water restrictions 4. 116 Snowfall 0 Source: through questionnaire Table 1: EWEs which impacts most in Delhi-NCR region. Identified impact areas are mentioned below: Logistics - for example vulnerability of supply chain, utilities and transport arrangements Premises - for example impacts on maintenance, facilities management or building design and construction People - for example implications for workforce or customers Process - for example impacts on concreting works and other construction works. [SEP] Justify:

Output: The text corroborates the claim by stating that respondents reported being affected by extreme weather events in the last two years, with specific examples such as heat waves, floods, and droughts affecting the region. The results imply that these extreme weather events (EWEs) have increased in both frequency and impact, aligning with the assertion that climate change contributes to more extreme weather events.


Input: Task: pass the claim using the given text. Claim: Unemployment rates can impact consumer spending and economic growth. [SEP] Text: 33 - Models; Spatio-temporal Models C34 - Truncated and Censored Models; Models C35 - Models; Discrete Regressors; Proportions C36 - (IV) Estimation C4 - Econometric and Statistical Methods: Browse content in C4 - Econometric and Statistical Methods: C41 - Duration Analysis; Strategies C43 - 5 - Browse content in C5 - C51 - 52 - Model Evaluation, Validation, and Selection C53 - Forecasting and Prediction Methods; C54 - Modeling C6 - Mathematical Methods; Programming Models; Mathematical and Browse content in C6 - Mathematical Methods; Programming Models; Mathematical and C60 - General C61 - Optimization Techniques; Programming Models; C62 - Existence and of Equilibrium C63 - Computational Techniques; C7 - Browse content in C7 - C71 - C72 - C73 - Stochastic and Dynamic Games; Evolutionary Games; C78 - Bargaining Theory; C8 - Methodology; Browse content in C8 - Methodology; C81 - Methodology for Collecting, Estimating, and Data; C82 - Methodology for Collecting, Estimating, and Data; C83 - Survey Methods; C9 - Design of content in C9 - Design of Experiments C90 - General C91 - Laboratory, C92 - Laboratory, C93 - D - content in D - Microeconomics D0 - content in D0 - General D00 - General D01 - Microeconomic Behavior: D02 - Institutions: Design, Formation, Operations, and Impact D03 - Behavioral Microeconomics: D04 - Microeconomic Policy: Formulation; Implementation, and Evaluation D1 - Browse content in D1 - D10 - General D11 - Consumer Economics: Theory D12 - Consumer Economics: D13 - D14 - Household Saving; D15 - Choice: Models and Saving D16 - D18 - D19 - Other D2 - Production and content in D2 - Production and Organizations D21 - Firm Behavior: Theory D22 - Firm Behavior: D23 - Organizational Behavior; Transaction Costs; D24 - Production; Cost; Capital; Capital, Total Factor, and Multifactor Productivity; Capacity D29 - Other D3 - content in D3 - Distribution D30 - General D31 - Personal Income, Wealth, and [SEP] Justify:

Output: The text is irrelevant to the claim as it provides a list of economic models and methodologies without discussing the impact of unemployment rates on consumer spending or economic growth. It does not address the claim or provide any supporting or contrasting information relevant to it.


Input: Task: pass the claim using the given text. Claim: Attention can be selectively focused, altering cognitive processing. [SEP] Text: 93 (SD = 36. 46; % of total trials = 18. 63). The number of trials rejected did not differ per condition (all p’s > 0. 152). Previous research (Grant et al. , 2015) suggested that the first and second halves of the task should be evaluated separately as encoding of the cue/picture pairing is ongoing in the first half, whereas the pairing has been encoded in the second. Thus, the first and second half of the task are separated for analyses. Analytic ApproachTo evaluate the impact of cuing and self-focused attention on anticipation and subsequent perception of pictures, two separate 2 (Cue: Self-vs. Standard Cue) × 2 (Stimuli Type: Negative vs. Neutral) × 2 (Task Half: H1 vs. H2) repeated-measures ANOVAs were used to evaluate SPN and LPP mean amplitude, respectively. Sphericity was assessed using Mauchly’s Test of Sphericity. The results section indicates where there are violations and in these instances the adjustments were made using the Greenhouse-Geisser correction. When examining pairwise comparisons, the Bonferroni adjustment was used when necessary to follow up for significant interactions. For brevity, only significant (p < 0. 05) effects are presented in the results section; however, all effects are presented in Table 1. Bayesian factors (BF) for each effect were calculated to determine the strength of the effect relative to the null hypothesis, such that BF values greater than 1. 00 suggest that data are more likely to occur under the research hypothesis, whereas BF values less than 1. 00 suggest that data are more likely to occur under the null hypothesis. Evaluation of the early posterior negativity (EPN) was also conducted and can be found in the supplement. Table 1 Repeated-measures ANOVA’s resultsFull size tableanalyses presented within this section were additionally computed when including worry, as indexed by scores on the (Meyer et al. , 1990), as a covariate based on research suggesting the impact of state anxiety on cued picture viewing. No significant interactions or main effects were observed when including this covariate. SPNResults showed a main effect of Cue (F(1,32) = 6. 45, p = 0. 016, ηp2 = 0. [SEP] Justify:

Output: The text does not address the claim directly; it discusses methodological aspects of a study involving attention and cuing effects without specifically stating how attention can be selectively focused or how that alters cognitive processing. Therefore, it is considered irrelevant to the claim.


Input: Task: corroborate the claim using the given text. Claim: Climate change is causing more extreme weather events worldwide. [SEP] Text: Finance - for example implications for investment, insurance and stakeholder reputation Markets - for example changing demand for goods and services As shown, in Table 2, Industries were most likely to be affected by the severe weather causing implications in terms of logistics (4. 843) and people (4. 719), process (4. 173). A smaller proportion also mentioned that the weather had affected premises (3. 129), finance (2. 207) and markets (1. 302). People 4. 719 Logistics 4. 843 Process 4. 173 Premises 3. 129 Markets 1. 302 Finance 2. 207 Source: through questionnaire Table 2: Imapct of EWE on Construction Industry. Most of the respondent mentioned impact on workers health/(4. 452) with also staff/key personnel not being able to get access to work or, simply access to work (3. 167). Problems caused by staff not getting into work were most pronounced in the times of Heat waves and cold waves, possibly due to a larger number of financially weaker labourers in this area who can’t cope up with changing weather conditions. The fact that the customers could not get to the business because of extreme weather (2. 389) and daily workers wage loss (2. 868) was also mentioned by respondents (Table 3). Impact on workers health/ 4. 452 Customers couldn't get to the 2. 389 business/site visits etc. Access to work of key 3. 167 personnel Daily workers wage loss 2. 868 (payee side) Source: through questionnaire Table 3: Impacts of Events on “People”. “Employees could not get to work. ” “Our staff had to clear the Water and Mud. ” “Our customers were not satisfied because project was late. ” “We were without staff for 2-3 days. ” “People were sent home because there was no electricity after the flooding. ” When asked to specify the greatest proportion of businesses who said extreme weather had impacted logistics mentioned supply of resources affected (3. 87) because of disruptions to either transport or access (3. 465) also causing delayed and delayed/failed deliveries (3. 111) (Table 4). Supply logistics Supply of resources affected 3. 871 Delayed/failed deliveries 3. 111 Transport disrupt/access 3. 465 Others like ICT related 2. [SEP] Justify:

Output: The text corroborates the claim by elaborating on the impacts of extreme weather events (EWE) on various sectors, particularly emphasizing the implications for logistics and worker accessibility. It states that 'the customers could not get to the business because of extreme weather' and mentions problems caused by staff unable to access work, particularly during heat waves and cold waves. This demonstrates that extreme weather events are affecting employment and operations due to climate change, supporting the claim that climate change is causing more extreme weather events worldwide.


Input: Task: pass the claim using the given text. Claim: Unemployment rates can impact consumer spending and economic growth. [SEP] Text:  M52 - Compensation and M53 - Training M54 - M55 - - Browse content in N - N1 - Macroeconomics and Monetary Economics; Industrial Structure; Growth; content in N1 - Macroeconomics and Monetary Economics; Industrial Structure; Growth; Fluctuations N10 - General, International, or Comparative N11 - U. [SEP] Justify:

Output: The text is irrelevant to the claim as it does not discuss unemployment rates, consumer spending, or economic growth. Instead, it lists content related to macroeconomics without providing any supporting or contrasting evidence regarding the claim.


Input: Task: pass the claim using the given text. Claim: Attention can be selectively focused, altering cognitive processing. [SEP] Text: 17) such that the SPN was attenuated following Self-(M = 0. 43, SE = 1. 24) compared with (M = 2. 86, SE = 1. 03). This was qualified by an interaction of Cue, Stimuli Type, and (F(1,32) = 6. 85, p = 0. 013, ηp2 = 0. 18). Follow-up pairwise comparisons were used to investigate this interaction, which revealed that the SPN amplitudes were reduced for Self-(M = 5. 45, SE = 2. 07) relative to (M = −4. 78, SE = 1. 72) for an upcoming unpleasant picture specifically during the latter half of the task. No other pairwise comparison was significant. Grand-average SPN waveforms are presented in Fig. 2. Fig. 2Grand averaged SPN waveforms and mean SPN amplitudes. Note. a SPN waveform from first half of trials. b SPN waveform from second half of trials. c and d SPN means and standard errors for first and second halves, respectively. H1 = first half. H2 = second half. Standard = Standard Cue. Self-Focus = Self-Focus Cue. Waveform scale is in microvolts (μV) and milliseconds (ms). SPN was measured from electrode Fz in the 200 ms before the onset of S2 and baselined using the 200 ms before the onset of S1. Yellow highlight on a and b represents measurement windowFull size imageLPPResults revealed a significant main effect of Stimuli Type, such that there was an enhanced LPP amplitude for unpleasant (M = 9. 67, SE = 1. 16) compared with neutral pictures (M = 4. 26, SE = 0. 89), F(1,32) = 47. 34, p < 0. 001, ηp2 = 0. 60. Grand-average LPP waveforms are presented in Fig. 3. Fig. 3Grand averaged LPP waveforms and mean LPP amplitudes. Note. a LPP waveform from first half of trials. b LPP waveform from second half of trials. c and d LPP means and standard errors for first and second halves, respectively. H1 = first half. H2 = second half. Standard = Standard Cue. Self-Focus = Self-Focus Cue. [SEP] Justify:

Output: The text is irrelevant to the claim as it primarily discusses statistical results related to SPN and LPP amplitudes in an experimental context without addressing the concept of selective attention or cognitive processing. There is no mention of attention allocation or its effects on cognitive processing in the provided text.


Input: Task: corroborate the claim using the given text. Claim: Climate change is causing more extreme weather events worldwide. [SEP] Text: 698 problems and Impact on schedule control Source: through questionnaire Table 4: Impact of EWEs on supply Logistics. ICT related problems and somewhat impact on schedule control were also notable issues with 2. 698 as the mean. As shown in the Table 5, it is site logistics that suffered mostly due to extreme weather events with increased difficulty in material storage, processing and handling difficulty (4. 342) followed by problems in coordination of material flows on the building site (3. 878). When EWE is sudden rainfall, Loss of resources (like CCTV cameras) (2. 749) and disposal of waste (3. 217) is an also problem due to marshy surface. Most of the comments were (Table 5): “Deliveries were held up - materials coming into the business and outgoing to customers. Staff also couldn't get into work. ” “The fog affected everything. The roads were inaccessible this meant that deliveries inbound and outbound to customers was affected. ” “We had trouble moving goods. ” “Production was slowed down because there were no staffs. ” Site logistics Material storage, 4. 342 processing and handling difficulty Coordination of material 3. 878 flow on construction site. Loss of resources 2. 749 Disposal of waste 3. 217 Source: through questionnaire Table 5: Impact of EWEs on Site Logistics. All the construction processes were significantly affected during extreme weather events except that of painting-flooring and tiling process (Table 6). Major problem is of mobility on premises (3. 9854) during the rainfall and low temperatures at the time of dense fog. Structural damage (3. 374) was also frequently occurring problem for businesses whose premises had been affected by extreme weather. Freezing and leaks (2. 585), floodwater loggings (3. 274) and even water scarcity (3. 161) were also mentioned by respondents (Table 7). Excavation-Foundation- 4. 129 Reinforcement placing Shuttering/ framework 4. 097 Concreting- Brickwork- 4. 129 (internal/external) 3. 023 Plaster of Paris 2. 112 Painting-Doors-windows- 1. 076 Flooring - Tiling Source: through questionnaire Table 6: Impacts of EWEs on processes at site. Damages to land/ structural 3. 374 damages/ Mobility on premises 3. [SEP] Justify:

Output: The text corroborates the claim by outlining various impacts of extreme weather events (EWEs) on logistics and construction site operations. It specifies difficulties such as increased problems with material storage, coordination of material flows, and significant mobility issues affecting staff and deliveries due to weather conditions like rainfall and fog. This supports the idea that climate change, contributing to more frequent extreme weather events, leads to operational challenges.


Input: Task: pass the claim using the given text. Claim: Interest rates set by central banks affect borrowing costs. [SEP] Text: 3−7. 2*−3. 7*−9. 7*−0. 4HBOS−0. 7*−0. 5*−0. 6*−0. 7*−6*−5. 8*−−−−HSBC−3. 5*−0. 4*−2. 2*0−34. 6*5. 9*−9. 4*−1. 2−8. 2*−2. 3*JPM−2. 4*0. 2*−3. 5*0. 1*−47. 4*−0. 7−11. 9*−0. 3−9. 8*−0. 4Lloyds−2. 3*0. 6*−2. 8*0. 2*−25. 2*−5. 5−7. 3*0N/AN/ANorinchuckin−0. 40. 1−0. 7*0. 1−1. 1−14. 7*0−0. 7−3*0. 3*Rabobank−3. 5*−0. 6*−2. 4*−0. 3−34. 8*−3. 7*−10. 5*0. 9−9. 6*0. 6*RBC−1. 9*−0. 1−1. 5*−0. 5−23. 4*0. 4−0. 2−3. 9*−2*−4. 9*RBS−2. 4*−0. 2−2*−0. 1−10. 6*−2. 1−0. 5−0. 3−4. 8*−1. 4*Societe Generale−−−−−−−8. 5*−0. 1−26. 3*20. 2*UBS−3. 4*0. 1*−0. 9*−0. 8*−26. 8*4. 5−4. 1*−1. 8*−9. 7*0. 2WestLB−1. 3*−0. 1−0. 7−0. 2−22. 5*1−1. 10. [SEP] Justify:

Output: The text consists of numerical data and bank-specific values that do not address the claim regarding the effect of interest rates set by central banks on borrowing costs. It provides no relevant context or explanation that would support or contrast the claim.