Input: Task: corroborate the claim using the given text. Claim: Climate change has contributed to more frequent and intense heatwaves. [SEP] Text: Observational studies have reported ecological changes in the region, such as shifts in the horizontal and vertical distributions of marine species8,19, as well as changes in pelagic micronekton and macrozooplankton communities and their species richness18,23. Such changes impacted also human activities such as fisheries19. Towards the end of 2019, a new MHW has emerged in the North Pacific24, raising concerns that a similar MHW as the Blob in 2013–2015 may reappear in the near future. Due to the already low numbers of Pacific cod (Gadus macrocephalus) and the potential reappearance of the Blob, the United States’ federal cod fishery in the Gulf of Alaska closed for the 2020 season as a precautionary measure25. The fisheries closure underscores the potential high impacts of such MHWs not only on marine ecosystems, but also on social-economic systems such as fisheries. Simulating ecological changes of fish stocks and fisheries using modelling approaches can help elucidate and attribute the relative contribution of MHWs to observed changes in ecosystems26 and assess future ecological risks under alternative scenarios of climate change27. However, projections of ecological impacts of MHWs have focused mainly on sensitive biogenic habitats such as coral reefs and intertidal systems28,29. In contrast, previous impact assessments on fish stocks and fisheries focused mainly on decadal-scale changes in mean conditions under climate change while the additional impacts of MHWs are more uncertain. Improved understanding of the futures of living marine resources will help inform dependent human communities, sectors and governance institutions to develop more effective climate-adaptation and risk-reduction measures. Here, we aim to test the hypotheses that MHWs will add to the impacts of changes in mean ocean conditions under climate change, leading to additional anomalous shifts in biomass, distribution and potential catches of fish stocks in the northeast Pacific regions. Previous studies have applied species distribution models to project the effects of changing long-term mean ocean conditions on spatial distribution, abundance, community structure and the potential biomass production of fishes and invertebrates in this region27,30,31. In this study, we extend such modelling approaches to examine the consequences of MHWs on fish stocks and fisheries. Our analysis focused on the northeast and the Ecosystems (LMEs) therein where most fishing took place (Fig. 1). [SEP] Justify:

Output: The text corroborates the claim by discussing the emergence of marine heatwaves (MHWs) and their significant ecological impacts in the context of climate change. It highlights how MHWs are projected to exacerbate the impacts of changing mean ocean conditions, indicating that climate change is contributing to these extreme heat events. The text notes that such MHWs lead to shifts in marine species distributions and have critical implications for fisheries, further establishing the link between climate change and heatwave frequency and intensity.


Input: Task: contrast the claim using the given text. Claim: Wave energy devices work efficiently in any part of the ocean. [SEP] Text: 16 shows an example of the effect of considering these factors for the wave climate at the (EMEC), where gross power refers to the omni-directional wave power, net power includes the effect of wave refraction and exploitable power also includes the effect of wave breaking and bottom friction. It can be seen that the difference in exploitable resource from a 50 m “deep water” site to a 10 m “shallow water” site is around 20 %, significantly less than the 50 % reduction in resource that can arise from an inappropriate use of the omni-directional wave power. More details on this case study are available in [17]. Fig. 3. 16Average incident wave power at the [17]Full size image The analysis and discussion above illustrate that the change in average omni-directional wave power from offshore to nearshore means that it is unlikely to be a good representation of the change in average power generation. Essentially, the wave climate in the nearshore cannot be considered as simply a less energetic version of the offshore wave climate (if this were the case then there would be more justification for the use of the average omni-directional wave power as a proxy for average power generation). Alternative representations have been proposed to compensate for this potential distortion. Thus, there is the average directionally-resolved wave power, which is the wave power resolved to a particular direction and also the average exploitable wave power, which is where the directionally-resolved wave power is limited to a fixed multiple (typically four) of the average. 3. 4 Measurement of Ocean Waves3. 4. 1 OverviewMeasurement of the ocean waves is clearly a very important part of understanding the wave energy resource. Although it is possible to estimate the expected waves at a particular location by analysing wave fields and modelling wave propagation, actual field measurements are required to both validate these estimates as well as provide information on the sea-state where an accurate model cannot currently be easily constructed. An example of the latter case would be measuring the impact of a wave energy converter on the down-wave conditions. Table 3. 1 contains a summary of the available systems and their characteristics. Table 3. 1 Wave measurement systemsFull size table The first three wave measuring instruments described above are deployed at the location of interest and use a time-series analysis to produce the wave spectrum. The duration used to define the wave spectrum depends on a number of competing factors. [SEP] Justify:

Output: The text contrasts the claim by stating that the wave climate differs significantly between offshore and nearshore environments. It explicitly notes that 'the wave climate in the nearshore cannot be considered as simply a less energetic version of the offshore wave climate', implying that wave energy devices may not work efficiently in all areas, indicating that efficiency can vary based on location and environmental conditions.


Input: Task: pass the claim using the given text. Claim: Functional MRI can measure brain activity by detecting changes in blood flow. [SEP] Text: CH in-bout, inter-ictal vs. HC: Stronger connectivity hypothalamus with inferior frontal gyrus, superior and middle temporal gyri, temporal pole, insula, parahippocampal gyrus, and uncus. Weaker connectivity hypothalamus with precuneus, inferior parietal lobule, and occipital lobe. Qiu et al. 62Episodic CH (n = 12) HC (n = 12)Regional homogeneityCH ictal vs. CH in-bout, inter-ictal: Increased regional homogeneity in posterior cingulate cortex and ventromedial prefrontal cortex. Decreased regional homogeneity in middle frontal gyrus, and parahippocampal gyrus. CH in-bout, inter-ictal vs. HC: Stronger regional homogeneity in anterior cingulate cortex, gyrus rectus, and orbital gyrus. Weaker regional homogeneity in middle prefrontal cortex, posterior cingulate cortex, insula, and dorsolateral prefrontal cortex. Rocca et al. 63Episodic CH (n = 13) HC (n = 15)(hypothalamus, thalamus)CH out-of-bout vs. HC: Connectivity differences in sensorimotor network and primary visual network. Increased connectivity of hypothalamus with anterior cingulate cortex, secondary sensorimotor cortex, primary visual cortex, middle occipital gyrus, thalamus, and insula. Increased connectivity of thalamus with primary sensorimotor cortex, supplementary motor area, and anterior cingulate cortex. Note: This table summarizes the methods and findings from published studies that have investigated brain functional connectivity in individuals with cluster headache. CH: cluster headache; HC: healthy control; ICA: independent components analysis; ROI: region of interest. Cluster vs. HCThe majority of functional connectivity studies have compared individuals with cluster headache to HCs. 45,59–64 Independent components analyses including a total of 68 individuals with cluster headache and 80 HCs from four different published studies have identified atypical connectivity in numerous resting state networks amongst those with cluster headache: salience, default mode, somatosensory, attention, visual, temporal, frontal, and cerebellar. [SEP] Justify:

Output: The text does not directly address the claim regarding functional MRI measuring brain activity through blood flow changes. Instead, it focuses on connectivity findings related to cluster headache and healthy controls, mentioning various regional homogeneity and connectivity patterns. Thus, the text is irrelevant to the claim.


Input: Task: corroborate the claim using the given text. Claim: Climate change has contributed to more frequent and intense heatwaves. [SEP] Text: For groundfish, biomass of Pacific cod (Gadus macrocephalus), sablefish (Anoplopoma fimbria) and Pacific ocean perch (Sebastes alutus) were projected to decrease significantly under MHWs in all LMEs. pollock in the Sea increased significantly in biomass under MHWs amongst all the 22 species and LMEs. Figure 4Projected mean changes in biomass, latitudinal and depth centroid of the 22 exploited marine fish stocks in the Sea (EBS), Gulf of Alaska (GoA) and (CC) during MHW years. NA - not available in catch record between 2006 and 2015. The different colour scales represent the projected changes in biomass, latitudinal centroid and depth centroid. Stripping of a cell represents changes at a significant level of below 0. 05. Full size imageFor MHW impacts on species’ biogeography, the distribution centroids of Pacific hering (Clupea pallasii pallasii) and sockeye salmon were projected to shift poleward in all three LMEs (Fig. 4). More fish stocks in Gulf of Alaska than in Sea or shifted significantly equatorward, or in variable directions amongst ensemble members under MHWs. Direction of shifts in depth centroid also often differed between LMEs for the same species. For example, sole (Microstomus pacificus) was projected to shift poleward in Sea and California Current, but equatorward in Gulf of Alaska and to deeper waters in Sea, while shifted shallower in Gulf of Alaska (Fig. 4). We chose Pacific cod, sockeye salmon and Californian anchovy that were of particular interest to fisheries and coastal communities in the northeast Pacific region to highlight how MHWs will exacerbate impacts from long-term climate change (Fig. 5). Firstly, these species will experience ocean warming as a result of both the mean increase in SST under RCP8. 5 as well MHWs (Fig. 5A–C). This will greatly exacerbate the warming hazards to these species. Secondly, biomass of these three fish stocks dropped approximately 5% for Pacific cod in Sea to 30% for sockeye salmon in Gulf of Alaska and California anchovy in during MHWs in addition to the decrease due to long-term mean changes under RCP8. 5 (5%, 25% and 10% by 2100 relative to 2000; Fig. 5D–F). [SEP] Justify:

Output: The text corroborates the claim by discussing how marine heatwaves (MHWs), which are linked to climate change, are projected to reduce the biomass of several fish species. It states that these species will also experience ocean warming due to both mean increases in sea surface temperature (SST) and MHWs, indicating that the impacts of climate change are being felt through more intense warming phenomena that lead to adverse effects on marine life.


Input: Task: contrast the claim using the given text. Claim: Wave energy devices work efficiently in any part of the ocean. [SEP] Text: The first factor is that shorter sampling durations result in a larger uncertainty in the estimated wave spectrum and sea-state parameters. For example, the standard uncertainty in the significant wave height based on a 15-min sample in the is about 15 %, with the standard uncertainty reducing proportionally with the square root of the sample duration [18]. The second factor is that the sea-state is continually changing, with the waves growing or subsiding with the varying wind fields. Consequently, excessively long sample durations would potentially result in extreme sea-states being “smoothed out”. The typical 15–30 min sample duration is generally considered to be a reasonable compromise between these two factors. The last of the factors is battery life and deployment time since powering the measuring instrument will limit the product of the sampling direction and number of sea-state records. For example, a wave-measuring buoy may be able to be used for a year deployment with 3 hourly records using 15 min sample durations, or a 2-month deployment reporting every hour using 30 min sample durations. The final choice of sample duration and frequency will depend on the purpose of the deployment, which should be carefully designed to ensure that the required information is obtained. 3. 4. 2 Surface-Following BuoyCurrently, the most common way of measuring ocean waves is with a surface-following buoy that is slackly moored to the seabed as shown in Fig. 3. 17. In these buoys, the vertical motion is typically measured using an accelerometer (although GPS systems are now becoming more common), which can then be double integrated to provide a time-series of the water surface elevation [19]. The recorded surface elevation is then used to estimate the wave spectrum and from that the sea-state parameters. Wave measurement buoys may also contain instruments to measure the inclination of the buoy so that the direction of the waves can be inferred and used to produce a directional wave spectrum, with associated directional sea-state parameters. A major benefit of using wave measuring buoys is that they have been used for a long time and thus their accuracy is well established. Their limitations are also relatively well recognised in that strong currents and steep waves both reduce the accuracy of the measurements because the buoy does not exactly follow the water surface. In addition, they are relatively expensive and there is a relatively high risk of loss of the instrument. Fig. 3. [SEP] Justify:

Output: The text contrasts the claim by discussing the limitations and uncertainties involved in measuring ocean waves with wave-measuring buoys. It highlights factors such as sampling duration, changes in sea-state due to varying wind fields, and accuracy issues in strong currents and steep waves. These points indicate that wave energy devices may not work efficiently in all parts of the ocean, particularly where conditions are extreme or variable.


Input: Task: pass the claim using the given text. Claim: Functional MRI can measure brain activity by detecting changes in blood flow. [SEP] Text: 45,59,63 All of these studies were performed when individuals with cluster headache were headache free, but some were done during the in-bout period and others during the out-of-bout period. Supporting the notion that the atypical connectivity was actually associated with cluster headache, these studies demonstrated correlations between the extent of atypical connectivity with duration that an individual had cluster headache and number of cumulative headache days. 45,59,63 Two studies including a total of 30 individuals with cluster headache and 31 HCs performed region-of-interest functional connectivity analyses of the hypothalamus. 61,64 In these studies, compared to HCs, individuals with cluster headache were found to have atypical hypothalamic connectivity with inferior, medial and middle frontal gyri, superior, middle, and inferior temporal gyri, temporal pole, inferior parietal lobule, insula, parahippocampal gyrus, fusiform gyrus, inferior semilunar lobule, occipital lobe, uncus, precuneus, and cuneus. There were correlations between the magnitude of atypical connectivity with the frequency of bouts per year. 64 Finally, a regional homogeneity study found that individuals with cluster headache have atypical regional homogeneity in anterior cingulate cortex, posterior cingulate cortex, insula, middle and dorsolateral prefrontal cortices, gyrus rectus, and orbital gyrus. 62Cluster in-bout vs. out-of-boutTwo studies, including a total of 35 individuals with episodic cluster headache, have investigated functional connectivity differences during a cluster period (i. e. in-bout) vs. between cluster periods (i. e. out-of-bout). 59,64 The independent components analysis demonstrated connectivity differences in frontal and dorsal attention networks and a correlation between functional connectivity of the frontal network with disease duration. 59 A hypothalamic region-of-interest analysis found differences in functional connectivity in-bout vs. out-of-bout with medial frontal gyrus, middle frontal gyrus, inferior temporal gyrus, precuneus and cerebellum. 64 Frequency of cluster bouts per year was correlated with connectivity strength of the hypothalamus with the cerebellum. Cluster during spontaneous attack (i. e. ictal) vs. in-bout but between attacks (i. e. [SEP] Justify:

Output: The text does not address the claim regarding functional MRI's ability to measure brain activity through blood flow changes. It discusses various studies related to cluster headache conditions and atypical connectivity without mentioning functional MRI techniques or their physiological underpinnings.


Input: Task: corroborate the claim using the given text. Claim: Climate change has contributed to more frequent and intense heatwaves. [SEP] Text: Similarly, shifts in biogeography, as indicated by the latitudinal centroids of the three selected species (Fig. 5G–I), added to the effects of the shifts due to changes in mean ocean conditions by as much as 100 km poleward during MHWs (e. g. , California anchovy in California Current). As such, biomass decrease and biogeographic shifts during MHWs early in the 21st century were projected to be at a similar level as the decadal-scale average changes by around the 2050 s. This also means that MWHs will exert large impact ‘shocks’ while fish stocks are already impacted by long-term mean climate change. For example, with both MHWs and changes in mean conditions, biomass of sockeye salmon was projected to drop by more than 40% by 2100 relative to 2000 under RCP8. 5. Figure 5Projected time-series of changes in sea surface temperature (SST) (A–C), biomass (D–F) and latitudinal centroid (G–I): Pacific cod (Gadus macrocephalus) (D,G), sockeye salmon (Oncorhynchus nerka) (E,H) and Californian anchovy (Engraulis mordax) (F,I)) in the Sea (EBS), Gulf of Alaska (GoA) and (CC). The solid lines represent the average values across the 10 ensemble member simulations (smoothed with a cubic spline function); blue-colored triangles represent values during MHW years; the different intensity of blue color represents different ensemble member simulations (see Figs. S1–12 for results for all the large marine ecosystems and studied fish stocks). Full size imageDiscussionOur findings provide theoretical support to the empirical observations from scientific surveys and anecdotal accounts from fishers that fisheries important fish stocks such as Pacific cod and sockeye salmon had been impacted by the 2013–2015 northeast Pacific MHW19. In addition, we offer new insights into the combined impacts of MHWs and long-term climate change on the species distribution in the northeast Pacific. Specifically, we show that MHWs can more than double the magnitude of the impacts on fish stocks by 2050 due to long-term climate change. Previous vulnerability and impact assessments have therefore greatly underestimated the risk to future fish stocks and fisheries in the northeast Pacific under climate change. [SEP] Justify:

Output: The text corroborates the claim by discussing how marine heatwaves (MHWs) are influenced by long-term climate changes and how they significantly impact fish stocks, indicating that climate change is linked to the increasing frequency and intensity of these heatwaves. It states that MHWs can amplify the effects of climate change on marine ecosystems, thus supporting the idea that climate change contributes to more frequent and intense heatwaves.


Input: Task: contrast the claim using the given text. Claim: Wave energy devices work efficiently in any part of the ocean. [SEP] Text: 17A surface-following wave measurement buoyFull size image 3. 4. 3 Sea-SensorA cheaper alternative to wave measuring buoys are sea-bed pressure sensors as shown in Fig. 3. 18. These instruments measure the variation in pressure and from that infer the water surface elevation [20]. The attenuation of wave pressure with depth means that they are only suitable for relatively shallow water, with the high-frequency waves being attenuated the most and thus sea-bed pressure sensors are most suitable for measuring swell waves. The exact water depth limit for sea-bed pressure sensors depends on the signal-to-noise ratio of the instrument, but a depth limit of about 10–20 m is typical. In addition to the waves, a sea-bed pressure transducer can also be used to determine the tide level, although in this case care should be taken to ensure that changes to the atmospheric pressure are accounted for. Finally, sea-bed pressure sensors may be deployed in an array to provide information on the directional distribution of the waves. Fig. 3. 18A seabed pressure transducer for wave measurement in shallow waterFull size image 3. 4. 4 ProfilerA more recently developed method of measuring waves is the use of acoustic current profilers, and in particular multi-beam acoustic current profilers as shown in Fig. 3. 19. Acoustic current profilers measure the water velocity using the red/blue shift in acoustic pulses for the instrument [21]. The water velocities as determined from each beam are combined and processed to produce a time-series of the 3D wave-induced water velocities and from that the directional wave spectrum. Although these instruments can be deployed in any orientation, it is normal for wave measurement to deploy them on the seabed where they are less susceptible to damage and bio-fouling. In this case the instrument must be deployed in less than about 50 m of water since they become less capable of detecting the wave-induced water velocities as they are further from the water surface. However, acoustic current profilers are developing rapidly, and it is possible that future improvements mean that they will be suitable for deployment in deeper water. An additional advantage of acoustic current profilers is that they also provide information on the local marine currents; however, a disadvantage that they share with sea-bed pressure transducers is that they typically store the data on board. [SEP] Justify:

Output: The text contrasts the claim by indicating that specific types of wave measurement devices, such as sea-bed pressure sensors and acoustic current profilers, have limitations regarding their deployment in water depth. For instance, sea-bed pressure sensors are suitable only for relatively shallow waters (10–20 m), and acoustic current profilers lose effectiveness in deeper water (beyond about 50 m). This suggests that not all wave energy devices can operate efficiently in any part of the ocean, as their performance is contingent upon the water depth and other environmental factors.


Input: Task: pass the claim using the given text. Claim: Functional MRI can measure brain activity by detecting changes in blood flow. [SEP] Text: interictal)Two studies have compared functional connectivity during cluster attacks vs. between attacks. 61,62 In a hypothalamic region-of-interest study of 12 individuals with episodic cluster headache, during a cluster attack there was increased connectivity to pain matrix regions such as the anterior cingulate cortex, posterior cingulate cortex, superior frontal gyrus, middle frontal gyrus, inferior frontal gyrus, superior temporal gyrus, inferior parietal lobule, parahippocampal gyrus, and amygdala. 61 In a regional homogeneity analysis of 12 episodic cluster subjects, during a cluster attack there was increased regional homogeneity of posterior cingulate cortex and ventromedial prefrontal cortex and decreased regional homogeneity of middle frontal gyrus and parahippocampal gyrus. 62In summary, functional connectivity studies of cluster headache have further implicated the hypothalamus as a region that participates in cluster headache pathophysiology (see Figure 4). As is expected with any headache disorder, several regions of the pain-matrix have also been shown to have atypical functional connectivity. However, functional connectivity aberrations associated with cluster headache reach beyond the hypothalamus, pain-matrix regions, and expected networks such as the somatosensory and salience networks. Studies have demonstrated that cluster headache is also associated with atypical functional connectivity within networks such as the default mode, attention, and visual networks. Individuals with cluster headache have altered functional connectivity compared to HCs, and functional connectivity differs when individuals are in-bout vs. out-of-bout, and during cluster headache attacks vs. between attacks. Although it could be hypothesized that the extent of functional connectivity “abnormalities” would increase as the cluster patient moves from the out-of-bout period, to the in-bout but inter-ictal period, to the within attack phase, there are currently inadequate data to confirm or refute this hypothesis. There are correlations between the frequency of cluster attacks and disease duration with the extent of atypical connectivity, strengthening an argument that the atypical connectivity is in fact related to having cluster headache and suggesting that there is a cumulative effect of cluster attacks on altering brain functional connectivity. Figure 4. A schematic illustration of regions and functional networks (default mode, salience, sensory, and attention) where studies have shown altered functional connectivity in patients with cluster headache compared to healthy controls. [SEP] Justify:

Output: The text does not directly address the claim regarding functional MRI's ability to measure brain activity through blood flow changes. Instead, it discusses functional connectivity studies related to cluster headaches and various brain regions involved without confirming or denying the claim about functional MRI's measurement capabilities.


Input: Task: corroborate the claim using the given text. Claim: Climate change has contributed to more frequent and intense heatwaves. [SEP] Text: Moreover, satellite data and model simulations suggest that MHWs are linked to and can be exacerbated by, multi-annual climate variability such as El Niño (ENSO), resulting in the particularly large and persistent biological impacts in the region from the Blob13,19,40. In any case, these complex biological responses of sardine and anchovy that inter-mixed between the effects of MHWs and decadal-scale warming therefore demand more rapid and short-term governance and adaptation responses such as alteration of fishing quota, shifts in fishing ground and targeted species41. The challenges from MHWs impact will thus put ‘double strains’ on sustainable management of living marine resources under climate change, pointing to the need for future research into the development of more robust adaptation and governance responses42,43. Previous studies have shown that global warming substantially increases the risks of MHWs to occur14. Our study additionally suggests that MHWs can strongly exacerbate the impact of decadal-scale mean ocean warming on fish stocks. A reduction of anthropogenic greenhouse gas emissions - the fundamental driver of global warming44 – is therefore needed to limit the impacts of MHWs on fish stocks and fisheries5. Even though we consider the projected pattern of MHW impacts on fish stocks and the implications for understanding future risks on fisheries and their governance under climate change as robust, a number of caveats needs to be discussed. The global Earth system model used in this study (i. e. 2M) is able to adequately simulate mean states and trends in different marine heatwave metrics over the satellite 1982–2016 period14. However, the horizontal resolution (about 1°) of the ocean component of the Earth system model is too coarse to accurately represent some of the oceanographic dynamics in coastal and shelf seas such as upwelling or mesoscale eddy activity e. g. 45. In addition, some of the biogeochemical processes in the high latitudes associated with sea ice are also not well resolved. The simulated net primary productivity in 2M, in particular, is highly uncertain46, especially in regions with sea ice47, because nutrient inputs during sea ice melt48 or through rivers49 are not included. The fish stock model assumes that historical species’ biogeography reflects their environmental niches50. Variations in the projected pathways of changes in biomass and biogeography of species in this study were partly caused by the differences in species’ temperature p51. [SEP] Justify:

Output: The text corroborates the claim by indicating that global warming substantially increases the risks of marine heatwaves (MHWs) to occur. It discusses how climate change and variability, such as El Niño, are linked to MHWs, which emphasizes the relationship between climate change and increased heatwave frequency and intensity. Additionally, it suggests a need for adaptation responses to manage these heatwave impacts, which further supports the idea that climate change plays a significant role in this phenomenon.


Input: Task: contrast the claim using the given text. Claim: Wave energy devices work efficiently in any part of the ocean. [SEP] Text: This means that the existence or quality of the wave resource data is not known until the instrument is recovered, which could be a problem if the data is critical for project development. Fig. 3. 19An acoustic current profilerFull size image 3. 4. 5 Land-Based and Satellite RadarIn addition to local wave measurement, it is also possible to use remote sensors, principally radar, to measure the waves, for example the 16-element HF radar array set up on the dike at Petten (NL) as shown in Fig. 3. 20. The main advantage of using a remote sensor is that the instrument does not need to be deployed in an aggressive environment, that the data is readily available and that the costs are typically lower [22]. The radar provides information on the sea-state by analysing the backscatter from the waves over an area. Coupled analysis from multiple locations means that the directional wave spectrum can often be calculated, together with sea-state parameters. Typically the radar system is calibrated using data from a local wave measuring instrument [23]. The use of radar for wave measurement is a rapidly developing area, with a range of different systems being developed and continually improved. Fig. 3. 20A land-based radar system for measurement of wavesFull size image A radar deployment of particular note is in satellites. Radar altimeters are available in a number of satellites that are circling the earth and can provide estimates of the Height along the track of the satellite [24]. Although the temporal resolution of the wave resource data may be very low (a satellite may only pass over a point every 10–35 days) it does provide wave resource data over a large geographical area. Although current satellite-based radar altimeter systems typically only provide information on the Height, as with other radar systems, the potential for satellite measurement of waves is increasing rapidly. Thus, current developments exist to use satellite data to produce estimates of the wave period as well as potentially other sea-state parameters. Interestingly, the satellite data can be used to compare the output from local wave measuring instruments and the consistency of these assessed. Whilst the Canadian and UK wave buoy networks share a common calibration factor, the US buoy network has a slightly different calibration factor. This implies that there is some difference in the calibration, deployment and/or operation of the wave buoys between the US and Canada/UK that results in a difference in the estimated sea-state. [SEP] Justify:

Output: The text contrasts the claim by highlighting that the existence or quality of wave resource data is uncertain until the wave measuring instruments are recovered. This uncertainty indicates that wave energy devices may not work efficiently in every location, as the quality of wave resources can vary significantly, affecting project development.


Input: Task: pass the claim using the given text. Claim: Functional MRI can measure brain activity by detecting changes in blood flow. [SEP] Text: In cluster headache, the hypothalamus has shown abnormal functional connectivity to regions of the default mode, salience, sensory and attention networks. Important regions of functional networks that are altered in cluster headache include the following:Default mode network regions: medial prefrontal cortex (mPFC) and anterior cingulate cortex (aCC), precuneus and posterior cingulate cortex (pCC) and the inferior parietal cortex (iPC). Salience network regions: insula and (dorsal) anterior cingulate. Sensory network region: somatosensory cortex (SS). Attention network regions: inferior frontal (iF) and middle frontal (mF) cortex, and the temporo-parietal junction (TPJ) and occipital cortex (OCC). Futures studies are needed that compare functional connectivity in cluster headache patients to individuals with other headache types to determine if certain findings are specific to cluster headache or if they are all shared by other headache types. For example, although the hypothalamus is strongly implicated in the pathophysiology of the TACs, it is also likely to be involved in migraine. 65,66 It is unknown if hypothalamic connectivity in the TACs is similar or different from connectivity in migraine. Future studies should investigate connectivity in individuals with chronic cluster as well as the other TACs. Since there can be a fair amount of overlap in clinical symptoms between the TACs, it would be useful to determine if functional connectivity patterns differ between individuals with different TACs. If so, it is possible that classification models developed from functional connectivity data could be used as diagnostic aids in the circumstances when the diagnosis is especially challenging via clinical grounds. is a worsening of a pre-existing headache disorder due to excessive intake of medication for the acute treatment of headache. 1 Most MOH patients have an underlying primary headache in the form of migraine or tension-type headache (TTH). Overall, it is estimated that MOH affects between 1% and 2% of the general population and at least 50% of chronic headache patients have MOH. 67The mechanisms underlying headache aggravation as a consequence of medication overuse are largely unknown. Clinical studies of pain perception in MOH patients indicate that central sensitization to nociceptive input is a key feature of this disorder. [SEP] Justify:

Output: The text does not directly address the claim about functional MRI's ability to measure brain activity via changes in blood flow. Instead, it discusses abnormal functional connectivity in regions of the brain relating to cluster headaches and suggests future studies on connectivity in headache disorders, without specifically confirming or denying the claim regarding fMRI functionality.


Input: Task: corroborate the claim using the given text. Claim: Climate change has contributed to more frequent and intense heatwaves. [SEP] Text: For instance, we projected a positive impact of MHWs on Alaskan pollock (T. chalcogramma) in Sea. However, previous studies have suggested that anomalous warm temperature affects the availability of preferred nutritious prey that reduced the survivorship and recruitment of pollock in the Bering Sea19. Moreover, we only examined climate projections following the ‘no mitigation’ high greenhouse gas emissions scenario (RCP8. 5) and including an ‘idealized’ fishing scenario i. e. , assuming all fishing is at level to achieve maximum sustainable yield of each fish stock. The effects of scenario uncertainties associated with different greenhouse gas emission and pathways of fishing effort and their management on the impacts of MHWs on marine ecosystems need to be explored further. Future research can build on the foundation laid by this study to incrementally address these uncertainties52. For example, the number of ensemble members, Earth system models and fish models may be increased to explore a wider range of model uncertainties. The analysis can also be repeated using high resolution Earth system models, and fish models with trophic interactions and/or eco-evolutionary dynamics. Overall, this study underscores the importance of considering MHWs in assessing climate risks and impacts. Previous risk and impact assessment that focused on the effects of long-term changes in mean conditions under climate change may have largely underestimated climate risks on fish stocks and fisheries. Moreover, the rapid rate of change and the prevalence of impacts across fisheries important fish stocks in the northeast Pacific point to the need to examine whether climate adaptation, designed mostly for dealing with long-term mean changes, would be sufficient to reduce the additional climate risks from MHWs. Without appropriate mitigation and adaptation measures, MHWs may pose additional risks on the long-term viability of marine species and the sustainability of their fisheries, and the associated benefits to dependent human communities such as food, economic benefits and livelihoods11. Our results also provide a foundation for further modelling efforts and analysis to build on and systematically explore different dimensions of uncertainties. MethodsEarth system modelWe analyzed projected changes in annual mean SST in the northeast using a 10-member ensemble simulation of the Earth system model version 2 developed at the (2M33,34,35). 2M is a fully coupled carbon cycle-climate model that consists of an ocean, atmosphere, sea ice, and land model, and includes land and ocean biogeochemistry. [SEP] Justify:

Output: The text corroborates the claim by discussing the impact of marine heatwaves (MHWs), which are a direct consequence of climate change, on marine ecosystems such as Alaskan pollock fisheries. It states that previous studies have indicated that anomalous warm temperatures associated with climate change affect the availability of prey and the overall survivorship and recruitment of fish stocks. Furthermore, it emphasizes the need for assessing climate risks and impacts, highlighting how MHWs can pose additional long-term risks due to climate change, thus supporting the assertion that climate change has led to more frequent and intense heatwaves.


Input: Task: contrast the claim using the given text. Claim: Wave energy devices work efficiently in any part of the ocean. [SEP] Text: The wave action, which is the wave energy divided by the intrinsic wave frequency, is used because it is conserved in the presence of background currents whilst the wave energy is not. However, in the absence of background currents, the wave action balance equation reduces to the wave energy balance equation. The wave action is allowed to propagate between four dimensions, geographic space, frequency space, directional space and time space (dependent on the physical processes involved), but the change in the sum of these must equal the source/sink of wave action, which may generate, dissipate and/or re-distribute the wave action. 3. 5. 2 Wave ModelsIn the absence of sources/sinks of wave action, then Eq. 3. 23 simply defines the wave kinematics and includes the processes of both shoaling and refraction. Depth-induced shoaling is the change in wave height with water depth and, in the absence of marine currents, it occurs due to the change in group velocity with water depth. When a wave enters shallow water, the group velocity changes, initially increasing slightly and then decreasing as the water depth decreases. The consequence of this is that the wave height initially decreases and then increases due to the conservation of energy as the energy gets stretched out and subsequently bunched up (note, it is commonly assumed that wave height only increases in shallow water since the initial decrease in wave height is only small and often not considered; however, it does occur). Depth-induced shoaling normally refers to the case when the wave is propagating orthogonally to the depth contours and so the wave does not change direction. Assuming a time-invariant condition, depth-induced shoaling is all included in the second term of the wave energy balance equation. When the wave is not travelling orthogonally to the depth contours, then depth-induced refraction occurs. Depth-induced refraction is also related to the change in wave group velocity with water depth. Indeed, it is possible to consider depth-induced shoaling as simply a special case of depth-induced refraction where the direction of wave propagation is parallel to the water depth gradient. If this is not the case, then the direction of wave propagation changes as the group velocity changes. Thus, when the wave group velocity starts to reduce in shallow water, the wave tends to rotate so that the wave crests become more parallel to the depth contours. [SEP] Justify:

Output: The text contrasts the claim by discussing the impact of varying conditions in the ocean on wave energy, particularly through depth-induced shoaling and refraction. It explains that wave energy behavior changes with water depth and current conditions, suggesting that efficiency may not be uniform across all locations in the ocean. For instance, it notes that wave height initially decreases and then increases when waves enter shallow water due to changes in group velocity, indicating that efficiency can vary based on geographic circumstances.


Input: Task: pass the claim using the given text. Claim: Functional MRI can measure brain activity by detecting changes in blood flow. [SEP] Text: 78MOH (n = 12) MYO (n = 11) HC (n = 16)ICA and seed-based (peri-aqueductal gray)Stronger connectivity in salience network (MOH > HC and MOH > MYO) and in inferior temporal network (MOH > HC and MOH > MYO). Weaker connectivity in frontoparietal network (MOH < HC). Stronger functional connectivity (MOH > HC) PAG with inferior frontal gyrus, middle frontal gyrus, cerebellum and stronger functional connectivity (MOH > MYO) of PAG with postcentral gyrus, pre- central gyrus, anterior cingulate cortex, temporo-parietal junction. Weaker functional connectivity (MOH < HC) of PAG with parahippocampal gyrus, precuneus, lingual gyrus, and fusiform gyrus. Note: Summary of methods and findings from published studies that have investigated brain functional connectivity in individuals with medication-overuse headache. MOH: medication-overuse headache; EM: episodic migraine; HC: healthy controls; HA: headache; MO: medication overuse; MYO: myofascial pain. One study investigated patients with MOH and migraine compared to patients with EM, and HCs. 75 The authors used a seed-based approach with the left precuneus as the region of interest to specifically study alterations of the default mode network. Compared to HCs, MOH and EM patients exhibited several regions that had either stronger or weaker connectivity with the precuneus. Interestingly, connectivity with the right hippocampus was stronger in MOH patients compared to both other groups. Of notice, MOH patients had higher self-rated medication-dependence, depression, anxiety, and pain-catastrophizing levels than episodic migraineurs, adding a potential risk of confounding. Precuneus-hippocampus connectivity was positively correlated with medication intake, suggesting that the findings could reflect a pharmaceutical effect. Another study76 compared resting state connectivity of MOH patients to other patients with similar headache frequency, but without medication overuse. [SEP] Justify:

Output: The text discusses various studies investigating brain functional connectivity in patients with medication-overuse headache, detailing findings related to connectivity patterns in specific brain regions. However, it does not directly address the claim regarding the capabilities of functional MRI to measure brain activity through blood flow changes. Therefore, it is irrelevant to the claim.


Input: Task: corroborate the claim using the given text. Claim: Climate change has contributed to more frequent and intense heatwaves. [SEP] Text: AbstractPlain is the heartland of modern China. This fertile plain has experienced vast expansion of irrigated agriculture which cools surface temperature and moistens surface air, but boosts integrated measures of temperature and humidity, and hence enhances intensity of heatwaves. Here, we project based on an ensemble of high-resolution regional climate model simulations that climate change would add significantly to the anthropogenic effects of irrigation, increasing the risk from heatwaves in this region. Under the business-as-usual scenario of greenhouse gas emissions, Plain is likely to experience deadly heatwaves with wet-bulb temperature exceeding the threshold defining what Chinese farmers may tolerate while working outdoors. China is currently the largest contributor to the emissions of greenhouse gases, with potentially serious implications to its own population: continuation of the current pattern of global emissions may limit habitability in the most populous region, of the most populous country on Earth. Similar content being viewed by others Limited influence of irrigation on pre-monsoon heat stress in the Indo-2. This region, inhabited by about 400 million, is one of the most densely populated in the world (Fig. 1). The rich soils in this region were formed by sedimentary deposits from the Yellow, Huai, and Hai rivers, providing excellent conditions for agriculture. The rainfall levels are relatively low compared to similar locations in South China, making irrigation necessary for supplementing soil water during the spring to early summer growing season (Fig. 1). This fertile plain has experienced vast expansion of irrigated agriculture which impacts significantly the surface radiation, surface energy balance, and boundary layer development in ways that impact surface humidity, and temperature. In observations that describe regional climate patterns, NCP stands out as a hot spot in the China-wide map of the maximum wet-bulb temperature (TWmax) observed in the last few decades (Fig. 1c). Given these past observations, the region is expected to remain vulnerable to heatwaves in the future. Fig. 1Brief characterization of Eastern China. Spatial distribution of a topographic map (m), b area equipped for irrigation (AEI, %) for 2005 from Data40 with climatology of annual precipitation from TRMM49 (contour, mm) in modern record (1998–2015), c highest daily maximum wet-bulb temperature from ERA-Interim41, TWmax (°C) in modern record (1979–2016), and d population density53 in people/km2. [SEP] Justify:

Output: The text corroborates the claim by stating that climate change would significantly add to the anthropogenic effects of irrigation, which increases the risk from heatwaves in the region. It highlights that under a business-as-usual scenario of greenhouse gas emissions, the area is likely to experience deadly heatwaves, suggesting a clear link between climate change and the frequency and intensity of heatwaves.


Input: Task: contrast the claim using the given text. Claim: Wave energy devices work efficiently in any part of the ocean. [SEP] Text: The rate of change on direction is defined by Snell’s law, which relates the change in direction of propagation with the change in wave group velocity. The consequence is that when the waves reach the shore, they are nearly parallel to the shoreline (they do not get to be completely parallel to the shoreline because non-linear processes cause them to break before they get to that point). Assuming a time-invariant condition, this process is modelled with the second and fourth terms of the wave energy balance equation. Where there are marine currents, then current-induced shoaling occurs when there is a change in the background current. The kinematics are more complex in this case because the change in current also causes a change in the relative (or intrinsic) frequency, which is the frequency of the waves as observed from a reference frame travelling at the same velocity as the current. In addition, there is an exchange of energy between the current and the waves, which is the reason Eq. 3. 23 is not defined in terms of wave energy, but in terms of wave action, which is conserved in the presence of changing currents. The exact current-induced shoaling can be determined by solving Eq. 3. 23 which shows that a following current tends to reduce the wave height as the energy is spread out, whilst an opposing current causes the wave height to increase as the energy bunches. If the opposing current is sufficiently strong, this can stop the waves propagating, and the energy increases until the waves start to break and thus lose energy. This can be seen commonly at the mouth of rivers where there is area of breaking waves as the current stops the waves from travelling upstream. For a time-invariant system, this is modelled with the second and third terms of the wave energy balance equation. As would be expected, marine currents can also be responsible for current-induced refraction. The explanation for this is similar to that of the depth-induced refraction, except that the change in velocity of the wave crest is due to spatial variation of the marine currents, rather than the change in water depth. In this final case, a time-invariant system the second, third and fourth terms of the wave energy balance equation are required to solve the wave kinematics. [SEP] Justify:

Output: The text contrasts the claim by explaining that factors such as marine currents can significantly affect wave behavior, indicating that wave energy devices may not work efficiently in all areas. It discusses current-induced shoaling, where waves can lose energy or change height due to interactions with currents. This suggests that there are conditions in the ocean where wave energy devices might not function effectively, thus undermining the blanket assertion made in the claim.


Input: Task: pass the claim using the given text. Claim: Functional MRI can measure brain activity by detecting changes in blood flow. [SEP] Text: While this is a rational design that is useful for uncovering mechanisms specific for MOH, the study is somewhat limited by a lack of specification of the underlying headache disorders in the MOH and non-MOH group and further that 6/15 of patients in the MOH group had less than 15 headache days/month, thus not fulfilling the ICHD criteria for MOH. 1 The authors investigated connectivity with the nucleus accumbens and the dorsal caudal and dorsal rostral putamen. The resulting connectivity maps served as input for a classification analysis, which could distinguish between the two groups of patients with 75% accuracy (based on nucleus accumbens) and 66% (dorsal rostral putamen). The authors suggested that these findings represent abnormalities of brain motivational circuits including the so-called reward system of the brain, which may relate to the addictive behavior in MOH. A study using a data-driven approach known as functional connectivity density (FCD)77 investigated MOH patients, primarily diagnosed with migraine, compared to groups of EM patients and HC. The authors found FCD differences between all three groups, but no overlapping differences between MOH vs. EM and MOH vs. HC. Most significant findings were reduced FCD in MOH compared to EM in the right caudate and left insula. Subsequent seed-based analysis based on these areas showed decreased connectivity to the left frontal inferior gyrus from both areas. To separate specific intrinsic connectivity changes of MOH to those of other chronic pain conditions, one study included patients with chronic myofascial pain (MYO) as well as MOH patients and HCs. 78 While MOH patients had a longer disease duration, pain intensity, anxiety and depression ratings did not differ between the two patient groups. The authors used ICA and a seed-based approach with the mid-PAG region as the seed region, motivated by the observation that the PAG shows reversible structural alterations in MOH. 70 Stronger functional connectivity was reported in the salience network (MOH>HC and MOH>MYO) and inferior temporal network (MOH > HC, MOH>MYO). In contrast, significantly weaker connectivity (MOH<HC and MYO<HC) was seen in the bilateral frontoparietal network (including the PAG) in both patient groups. [SEP] Justify:

Output: The text does not address the claim directly and instead focuses on the limitations and findings of a study regarding patients with Medication Overuse Headache (MOH) and chronic pain conditions. While it mentions functional connectivity and brain circuits, it does not confirm or deny the ability of functional MRI to measure brain activity via blood flow changes, making it largely irrelevant to the claim.


Input: Task: corroborate the claim using the given text. Claim: Climate change has contributed to more frequent and intense heatwaves. [SEP] Text: The box in each plot indicates the Plain used for regional analysis in this study. The figure was created using the ( size imageThe frequency and intensity of heatwaves observed in China has increased significantly during the last 50 years as documented by several studies3,4,5,6. Severe heatwaves have been experienced in China, particularly since the beginning of the twenty-first century5. The surface mean temperature averaged over China has increased by about 1. 35 °C during 1951–2006, a rate of about 0. 24 °C/decade7, which is larger than the corresponding global rate of about 0. 13 °C/decade during 1956–20058. and August of 2003, extremely hot weather conditions lasting for 20–50 days occurred at many sites over South China9. During this period, the daily maximum temperature recorded in many locations south of was above 38 °C10. Heatwaves extended across most of China in 2006, with daily maximum temperature reaching 35 °C at 22 stations in Chongqing, Central China11. In summer 2013, a severe heatwave event occurred in Eastern China12. The daily maximum temperatures in many stations were extreme, breaking the historical records. Around Shanghai, the 141-year temperature record was exceeded, and the heatwave resulted in the death of dozens of people13. Studies on heatwaves often (but not always) focus on surface temperature as the main variable used for characterizing their intensity14,15. However, consideration of surface humidity is as important as that of surface temperature in defining heatwaves, since humidity impacts how humans feel heat stress. Building on earlier work15 that defined a threshold for human survivability based on the magnitude of TW (an integrated measure of temperature and humidity), we have recently adopted the use of the daily TWmax, computed from a 6-h moving average time series, for characterizing the intensity of heatwaves16,17. The choice of TW is motivated by the fact that the skin of a sweating human body can be approximated by this temperature, and the choice of 6 h is rooted in the assumption that a healthy human may not survive outdoors at a TW of 35 °C for more than 6 h. Hence, TWmax of 35 °C is assumed as physiologic threshold for survival of humans18. [SEP] Justify:

Output: The text corroborates the claim by providing evidence that the frequency and intensity of heatwaves in China have increased significantly over the last 50 years, indicating a direct impact of climate change. It mentions that severe heatwaves have been experienced particularly since the beginning of the twenty-first century and details specific instances of extreme temperatures, such as surpassing historical records in 2013 and the significant temperature increase of about 1.35 °C averaged over the period from 1951 to 2006. This information supports the proposition that climate change is linked to more frequent and intense heatwaves.


Input: Task: contrast the claim using the given text. Claim: Wave energy devices work efficiently in any part of the ocean. [SEP] Text: A major limitation of spectral wave models is that they assume a random phase between the wave components, although this is also the assumption that allows them to be computationally efficient for modelling wave transformations over large distances. Consequently, phase-dependent processes such as harbour resonance and diffraction cannot be modelled explicitly. However, whilst no approximation for processes such as harbour resonance exist for spectral wave models, it has been found that diffraction can be modelled using a phase-decoupled refraction-diffraction approximation [31]. This approximation is based on the mild-slope equation where the turning rate of the wave due to diffraction is dependent on the slope of the wave amplitude, and has been found to be a reasonable approximation in the majority of circumstances except locally close to the body causing the diffraction field. 3. 5. 3 third generation spectral wave models (for example SWAN, Mike21), there are typically six types of wave energy source/sink. These are wind input, quadruplet wave-wave interactions, white-capping, bottom friction, triad wave-wave interactions and depth-induced wave breaking, cf. Fig. 3. 21. Fig. 3. 21Source terms used in a third-generation spectral wave modelFull size image These sources/sinks are, in general, represented by semi-empirical approximations. In many cases, a number of alternative representations can be used in the wave models; the choice being somewhat dependent on the p3. 5. 4 Grid DefinitionWith the definition of the conservation equation for the action density, together with the source terms that add and remove energy, it is only necessary to propagate the action density from one point to another in order to define the wave resource. This may be done using a regular or irregular grid, using a number of different propagation schemes, each of which have their own particular set of advantages and disadvantages. The resolution of the grid will depend on the desired accuracy of the model, with higher resolution grids typically resulting in more accurate models, but at the expense of greater computational effort. However, the change in accuracy with grid resolution also depends on the relative magnitude of the components in the action density equation, so that where the action density changes little a low resolution grid may be used, but higher resolution grids are required where the changes in action density are greater. This naturally leads to the use of irregular (or nested regular) grids as shown in Fig. 3. [SEP] Justify:

Output: The text contrasts the claim by discussing the limitations of spectral wave models in predicting wave behavior. It highlights that certain processes affecting wave energy, such as harbour resonance and diffraction, cannot be explicitly modeled under specific assumptions made by these models. The mention of different grid definitions and the requirement for higher resolution grids for accuracy suggests that the efficiency of wave energy devices may not be consistent in all ocean environments, thereby rebutting the claim that they work efficiently in any part.


Input: Task: pass the claim using the given text. Claim: Functional MRI can measure brain activity by detecting changes in blood flow. [SEP] Text: There is strong suggestion that atypical connectivity of regions that are part of the brain reward system is uniquely involved in MOH. The hypothalamus plays an important role in cluster headache and other TACs and hypothalamic functional connectivity is clearly atypical in the presence of these headaches. However, it is likely that the hypothalamus also plays an important role in migraine and it is not clear if hypothalamic activity and functional connectivity differ between the TACs and migraine. More studies that directly compare functional connectivity across headache types are needed. The comparison of studies specific to a headache disorder and the comparison of resting-state data between headache disorders is complicated by patient heterogeneity (differences in age, depression, anxiety, demographic, psychological background) as well as by differences in data acquisition (variability due to field strength, 1. 5 Tesla versus 3 Tesla; or differences in imaging protocols or scanner vendors). Comparison between studies is further complicated by differences in study design and data post-processing protocols (differences in statistical thresholding, or power calculations). Whereas some studies have used a region-of-interest approach, other studies have used an ICA. Both have inherent strengths and weaknesses. Whereas the seed-based approach allows interrogation of specific functional connections, it is also less robust than ICA, which can determine large-scale networks automatically without a priori hypothesis. Furthermore, seed-based approaches might not specifically interrogate a ‘network per se’ as certain regions can be a part of more than one network. 79 Other studies have interrogated different frequency bands of BOLD fluctuations, believed to indicate yet another way of interrogating fluctuations in frequency-specific functional connections. 80 Direct comparison amongst different rs-fMRI studies is challenging within any area of research due to variability amongst studies relating to the use of imaging designs, protocols, and post-processing techniques. The heterogeneity of study results is certain to relate to the relatively small sample sizes, as most of the rs-fc studies include less than 20 subjects in each group. Future studies, using replication designs as well as larger multicenter studies will be useful to validate and to solidify our current understanding of the pathophysiology underlying headache. Over the past decade, headache neuroimaging using rs-fMRI has become a rapidly growing field and recent studies have shown encouraging results for distinguishing individual patients with migraine from HCs using classification algorithms based on rs-fMRI data or rs-fMRI plus structural MRI data. [SEP] Justify:

Output: The text discusses issues related to functional connectivity in brain imaging related to headache disorders but does not directly address or confirm the claim regarding the ability of functional MRI to measure brain activity by detecting changes in blood flow. Instead, it focuses on challenges faced in studying functional connectivity among different headache types and the variability in research results, making it irrelevant to the claim.


Input: Task: corroborate the claim using the given text. Claim: Climate change has contributed to more frequent and intense heatwaves. [SEP] Text: The choice of this variable has important implications regarding where we may expect to see severe heatwaves, since a typical TW (dry-bulb temperature) for a forest would be significantly warmer (colder) than the corresponding value for a desert along the same latitude. Previous studies concluded that irrigation impacts the land-surface energy balance and atmospheric boundary layer development significantly19,20. The direct effects of irrigation are to enhance evapotranspiration, to cool the surface temperature, and to moisten the air21,22. The cooler surface emits significantly less long-wave radiation upward (Stephan–Boltzmann law), and the moistened air emits significantly more long-wave radiation towards the surface (water vapor greenhouse effect). As a result, irrigation tends to enhance the net long-wave radiation available to fuel total flux of latent and sensible heat into the atmosphere. Similarly, irrigation may impact how the surface absorbs incoming solar radiation23, but the sign of that effect would depend on the nature of land cover before irrigation. In addition, irrigation impacts the development of the atmospheric boundary layer23. Irrigation reduces the sensible heat flux resulting in lowering of the depth of the atmospheric boundary layer. Hence, irrigation enhances the total flux of heat only slightly, but results in a significantly shallower boundary layer. Considering the total energy of the surface air in the atmospheric boundary layer, including latent and sensible forms, as measured by the TW over extensively irrigated plains such as those in the NCP, we would expect an enhanced magnitude of the boundary layer energy per unit mass as measured by variables such as moist static energy or the TW. In two recent studies24,25, the impact of climate change on heatwaves was investigated at the global scale, using observations and simulations of surface temperature and humidity. was identified in one of these studies25 as one of the impacted regions, the relatively coarse resolution and global focus of their analysis precluded identification of hotspots for heatwaves at regional and local scales within China. The same studies did not include analysis of the impact of irrigation on heatwaves. The high resolution of our simulations, including representation of irrigation processes, makes it feasible to address these two important topics. [SEP] Justify:

Output: The text corroborates the claim by discussing recent studies that investigated the impact of climate change on heatwaves at a global scale. It mentions that these studies analyzed surface temperature and humidity, which are critical factors linked to the frequency and intensity of heatwaves. Although the specific effect of irrigation on heatwaves is noted, the text overall supports the notion that climate change is a contributing factor to heatwave occurrences.


Input: Task: contrast the claim using the given text. Claim: Wave energy devices work efficiently in any part of the ocean. [SEP] Text: The small wave period case represents, for the RHPSC, the worst case of approximation, since the ratio between the period of the waves and the control horizon is small. Thus, more HRCF basis functions are needed to correctly approximate the state and control variables. One can see that after 21 basis functions, corresponding to \(N = 10\), the normalised absorbed energy has already converged, whereas the computational time needed to solve the QP is increasing. A reasonable choice for the number N is, therefore, found around 10, leading to 21 HRCF basis functions involved in the RHPSC algorithm. Fig. 6Normalised absorbed power and QP computational time as a function of the number of HRCF basis functions in the RHPSC simulationFull size image Figure 7 presents the evolution of the normalised absorbed energy and the QP computation time for the RHPSC, plotted against the duration of the control horizon T. One can observe that, in contrast with the MPC algorithm, the computational time does not depend on the control horizon, since the number of basis functions are fixed, and consequently the size of the QP computational effort does not change as well. A reasonable choice for the control horizon will be a duration of 20 seconds. Fig. 7Normalised absorbed power and QP computational time as a function of control horizon for the RHPSC methodFull size image Based on the selection of parameters for the MPC and RHPSC methods, as described in Sects. 5. 1 and 5. 2, respectively, comparisons between the presented methods are now performed with both regular and irregular sea states. For the remainder of the study, the chosen parameters and control windows are fixed as shown in Table 1. 6 Relative performance in regular wavesBased on the parameters defined in Table 1, the MPC and RHPSC algorithms were tested for regular waves with a wave period varying from 5 to 15 s. The evolution of the absorbed energy and the computational time involved in the QP optimisation, as a function of the wave period, is presented in Fig. 8. The model used in the simulation evaluation loop is different from the dynamical model used to define the control algorithm, for both MPC and RHPSC. [SEP] Justify:

Output: The text contrasts the claim by discussing the performance of wave energy devices in varying wave conditions, indicating that the efficiency is not uniform across different scenarios. Specifically, the text mentions that the small wave period case represents 'the worst case of approximation,' suggesting that there are specific conditions (like small wave periods) under which these devices do not operate efficiently, implying that efficiency is conditional rather than universal.


Input: Task: pass the claim using the given text. Claim: Functional MRI can measure brain activity by detecting changes in blood flow. [SEP] Text: 81,82 Another study has shown utility of rs-fMRI data for characterizing migraine patients with MOH from patients without MOH. 76 Results of these studies show the utility of rs-fMRI for querying headache pathogenesis as well and indicate future potential of rs-fMRI for identifying individual patients with headache, thus showing potential for headache classification. Declaration of conflicting interestsThe author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this 1. Classification of Headache Disorders, 3rd edition (beta version). Cephalalgia 2013; 33: 629–808. 2. Rating of olfactory judgements in migraine patients. Cephalalgia 2006; 26: 1123–1130. CrossrefPubMedWeb of Science3. Cutaneous allodynia in the migraine population. 2008; 63: 148–158. CrossrefPubMedWeb of Science4. Photic hypersensitivity in the premonitory phase of migraine–a positron emission tomography study. Neurol 2014; 21: 1178–1183. CrossrefPubMedWeb of Science5. Concurrent functional and structural cortical alterations in migraine. Cephalalgia 2012; 32: 607–620. CrossrefPubMedWeb of Science6. Schmitz N, Admiraal-Behloul F, Arkink EB, et al. Attack frequency and disease duration as indicators for brain damage in migraine. Headache 2008; 48: 1044–1055. CrossrefPubMedWeb of Science7. Schmidt- Subtle grey matter changes between migraine patients and healthy controls. Cephalalgia 2008; 28: 1–4. CrossrefPubMedWeb of Science8. Thalamo-cortical network activity during spontaneous migraine attacks. Neurology 2016; 87: 2154–2160. CrossrefPubMed9. Gil-. Cognitive dysfunction during migraine attacks: a study on migraine without aura. Cephalalgia 2015; 35: 662–674. CrossrefPubMedWeb of Science10. Gil-. Subjective cognitive symptoms during a migraine attack: a prospective study of a clinic-based sample. 2016; 19: E137–E150. PubMed11. Resting state connectivity between default mode network and insula encodes acute migraine headache. ahead of print 1 January 2017. DOI: 10. [SEP] Justify:

Output: The text is irrelevant to the claim as it discusses the utility of resting-state functional MRI (rs-fMRI) in characterizing migraine patients and does not explicitly address how functional MRI measures brain activity through blood flow changes. It does not provide evidence or commentary on the mechanisms of functional MRI but instead focuses on its potential applications in the context of headache classification.


Input: Task: corroborate the claim using the given text. Claim: Climate change has contributed to more frequent and intense heatwaves. [SEP] Text: In this study, we project based on an ensemble of high-resolution regional climate model simulations that climate change would add significantly to the anthropogenic effects of irrigation, increasing the risk from heatwaves over the NCP region, an area with extensive irrigation development covering a relatively large fraction of the land surface. Under the business-as-usual (BAU) scenario of greenhouse gas emissions, NCP is likely to experience deadly heatwaves with TW exceeding the threshold defining what Chinese farmers may tolerate, while working outdoors without air conditioning. ResultsModel experiments and evaluationHere, we use the of Technology (MIT) Model (MRCM)26 with lateral boundary conditions obtained from simulations by a carefully selected set of global climate models from among those that participated in CMIP527 (see selection criteria described in the Methods section and 1 for the list of global climate models and their details). We perform simulations for historical period (1975–2005), as well as future climate (2070–2100) assuming two scenarios of GHG emissions28 (BAU scenario (RCP8. 5) and moderate mitigation scenario (RCP4. 5)) (see Supplementary Fig. 1, for simulations domain, and 2). For the historical and future period, we performed six sets of simulations, with and without irrigation. By comparing the historical period simulations, with and without irrigation, we estimate the impact of irrigation on heatwaves in the historical climate period as described by the TW. The results of these simulations reveal a significant role for irrigation in enhancing the magnitude of extreme TWmax and hence the intensity of heatwaves. (By “extreme” we mean maximum simulated value over this period. ) Over the irrigated region and NCP, the extreme TWmax over a 30-year period increases by about 0. 5 and 0. 3 °C, respectively, as a result of irrigation during historical period. This impact of irrigation is even larger, if we consider the average TWmax instead of extreme values, and more pronounced during the relatively drier months of early summer (May and June) (see Supplementary Figs. 2–4). These are the months when irrigation has the largest impact on land surface conditions. Heatwaves due to climate change and irrigationThe impacts of climate change are significantly larger than those of irrigation. The extreme TWmax over irrigated area and NCP are projected to increase by an additional 3. 4 and 3. [SEP] Justify:

Output: The text corroborates the claim by stating that climate change is projected to significantly increase the risk from heatwaves, particularly in regions with extensive irrigation, and that the impacts of climate change on the extreme temperatures associated with heatwaves are significantly larger than those of irrigation. For instance, it notes that under scenarios of greenhouse gas emissions, the extreme temperature maximum (TWmax) is projected to increase, which aligns directly with the claim that climate change has contributed to more frequent and intense heatwaves.


Input: Task: contrast the claim using the given text. Claim: Wave energy devices work efficiently in any part of the ocean. [SEP] Text: In the simulation evaluation, the convolution product involved in the determination of the radiation force is computed by a direct integration of the product between the past values of the real WEC velocity and the kernel function of radiation obtained from a boundary element method, to ensure an even comparison with maximum fidelity of the radiation force. Table 1 Values of the MPC and RHPSC parametersFull size table Both MPC implementations lead to comparable results; their energy absorption lies between 85 and 100 % of the theoretical maximum. The computational time does not depend on the wave period of the incoming waves and is around 15 ms. Since the time-step of the MPC algorithm is equal to \(\Delta t = 0. 1\) s, the control can be run in real-time. RHPSC presents approximately the same energy absorption and recovers between 90 and 100 % of the theoretical maximum. The computational time involved in the QP optimization does not vary with the wave period of incoming regular waves and is found to be around 5 ms. presents relatively fast convergence property and reaches the same rate of energy absorption as a standard MPC algorithm, with a computational time around three time smaller. Fig. 8Normalised absorbed power and QP computational time for MPC and RHPSC algorithm for different regular wave periodsFull size image The energy absorption performance of both control algorithms under path constraints is presented in Fig. 9. For these simulations the position of the device is limited to \(\pm 0. 1\) m and its velocity to \(\pm 0. 1\) m/s. The same control tuning defined in Table 1 is employed and the waves are regular, with periods between 5 and 15 s. The impact of the constraints on the energy absorption is similar for both the MPC and the RHPSC. For small wave periods, around 5 s, the motion of the device does not exceed the constraints, and thus the constraints do not impact the energy absorption, found to be around 95 % of the theoretical maximum. When the wave period increases, for a fixed wave amplitude, the excitation force increases as well. Yet, the natural motion of the device purports to exceed the position and velocity limitations, and the impact on the energy absorption becomes more significant as the wave period increases. The normalised energy absorption drops from 95 % for a 5 s wave period to 15 % for a 15 s wave period. [SEP] Justify:

Output: The text contrasts the claim by describing how the energy absorption efficiency of wave energy devices is affected by the wave period. It explicitly states that energy absorption drops significantly from 95% for a 5-second wave period to 15% for a 15-second wave period, indicating that these devices do not work efficiently in all conditions of the ocean. The discussion of performance under varying wave periods suggests that efficiency is not consistent across different oceanic environments.


Input: Task: pass the claim using the given text. Claim: Functional MRI can measure brain activity by detecting changes in blood flow. [SEP] Text: 1177/0333102417715230. Crossref12. Change in brain network connectivity during PACAP38-induced migraine attacks: a resting-state functional MRI study. Neurology 2016; 86: 180–187. CrossrefPubMed13. Increased intrinsic brain connectivity between pons and somatosensory cortex during attacks of migraine with aura. Mapp 2017; 38: 2635–2642. CrossrefPubMed14. Brain stem activation in spontaneous human migraine attacks. 1995; 1: 658–660. CrossrefPubMedWeb of Science15. . Epidemiology of headache. Neurol 2010; 97: 3–22. CrossrefPubMed16. Foroozan R, Cutrer FM. Transient neurologic dysfunction in migraine. 2009; 27: 361–378. CrossrefPubMed17. Clinical characteristics of 362 patients with familial migraine with aura. Cephalalgia 2004; 24: 564–575. CrossrefPubMedWeb of Science18. Mechanisms of migraine aura revealed by functional MRI in human visual cortex. 2001; 98: 4687–4692. CrossrefPubMedWeb of Science19. Interictal brain activity differs in migraine with and without aura: resting state fMRI study. J 2017; 18: 8. CrossrefPubMed20. Measurement of visual sensitivity in migraine: validation of two scales and correlation with visual cortex activation. Cephalalgia 2015; 35: 585–592. CrossrefPubMedWeb of Science21. Aurora SK, Wilkinson F. The brain is hyperexcitable in migraine. Cephalalgia 2007; 27: 1442–1453. CrossrefPubMedWeb of Science22. Reduced functional connectivity between salience and visual networks in migraine with aura. Cephalalgia 2016; 36: 53–66. CrossrefPubMedWeb of Science23. Gilbert CD, Li W. Top-down influences on visual processing. Neurosci 2013; 14: 350–363. CrossrefPubMedWeb of Science24. Multimodal imaging evidence for a frontocortical modulation of visual cortex during the selective processing of conditioned threat. J 2017; 29: 1–15. CrossrefPubMed25. Increased interictal visual network connectivity in patients with migraine with aura. Cephalalgia 2016; 36: 139–147. [SEP] Justify:

Output: The text is irrelevant to the claim as it provides a list of studies related to migraines and functional MRI, but does not directly address or explain how functional MRI measures brain activity through changes in blood flow.


Input: Task: corroborate the claim using the given text. Claim: Climate change has contributed to more frequent and intense heatwaves. [SEP] Text: 3 °C, respectively, assuming a BAU scenario of greenhouse gas (GHG) emissions with irrigation, while increases of extreme TWmax are 2. 9 and 3 °C over irrigated area NCP, respectively, without irrigation (Fig. 2). The spatial distribution of TWmax under current and future climate features three regions with significantly warmer conditions: the NCP close to the Eastern coast, the Yangtze river valley, and the Southern coast. All these regions are characterized by relatively low elevation (lower than 50 m) compared to the surrounding area (e. g. , (Fig. 1a)), which is a major factor explaining occurrence of relatively warm conditions. Over several locations in the NCP and along the Eastern coast of China, such as the areas around and Hangzhou under the BAU scenario, TWmax exceeds the critical threshold for human survival of 35 °C, during several episodes over a 30-year period (Fig. 3). Moderate climate change mitigation efforts, represented by the RCP4. 5 scenario of GHG emissions, reduce the risk of such heatwaves significantly; however, deadly heatwaves are still projected even under those conditions, though significantly less frequent (Fig. 3). In interpretation of the results of this study, we emphasize that TWmax values as low as 30 °C would qualify as “Extremely Dangerous” according to the (NOAA) (see 6). Fig. 2Spatial distribution of extreme wet-bulb temperature. Ensemble average of the 30-year maximum TWmax (°C) for irrigation activity and each GHG scenario: historical without irrigation activity (a), RCP4. 5 without irrigation activity (b), RCP8. 5 without irrigation activity (c), historical with irrigation activity (d), RCP4. 5 with irrigation activity (e), and RCP8. 5 with irrigation activity (f). Averages for irrigated region (IRR) and Plain (box in plot, NCP) are indicated in each plot. Extent of irrigated area is shown in Supplementary Fig. 1. TWmax is the maximum daily value from 6-h running average for each day (bias correction described in the Methods section). The figure was created using the ( size imageFig. 3Histogram of daily maximum wet-bulb temperature in 16 cities over Eastern China. [SEP] Justify:

Output: The text corroborates the claim by discussing how climate change, specifically through greenhouse gas emissions, leads to increased maximum wet-bulb temperatures (TWmax) across various regions in China. It states that under a business-as-usual scenario, TWmax exceeds critical survival thresholds multiple times during a 30-year period, which indicates an increase in heatwave frequency and intensity related to climate change.


Input: Task: contrast the claim using the given text. Claim: Wave energy devices work efficiently in any part of the ocean. [SEP] Text: This is due to the fact that the MPC and RHPSC tunings are based on performance determined under regular wave constraints in a [5 s,15 s] period range. Fig. 10Normalised absorbed power and QP computational time for MPC and RHPSC algorithm for different irregular sea statesFull size image To illustrate the ability of the MPC of RHPSC algorithms to maximize the energy production under unconstrained conditions, time series of the position, velocity, control force and absorbed energy are presented in Fig. 11, for an irregular sea state with a peak period of 10 s. Both MPC and RHPSC trajectories are compared to the theoretical optimal one and present a relatively acceptable agreement. Oscillations can be noticed, in particular for the RHPSC, at the beginning of the simulation. The control algorithms starts to drive the WEC with an initial condition of zero, i. e. position and velocity null at the origin of time, while the theoretical optimal trajectory begins with non-null initial conditions. This difference induces a transient state necessary for the RHPSC control algorithm to reach the optimal trajectory from null initial conditions. Fig. 11Normalised absorbed power and QP computational time for MPC and RHPSC algorithms for different irregular sea statesFull size image MPC and RHPSC are particularly interesting and practical control algorithms since they can handle constraints, or technological limitations, while maximizing the energy production. To illustrate the ability of the MPC and RHPSC algorithms under constrained conditions, the time series of the control and state variables, and their technological limitations, are show in Fig. 12 for an irregular sea state with a peak period of 10 s. Fig. 12Normalised absorbed power and QP computational time for MPC and RHPSC algorithms for different irregular sea states under path constraintsFull size image 8 DiscussionBased on the results presented in Sects. 6 and 7 for regular and irregular waves, some insights and further improvements can be deduced from these comparisons. The MPC algorithm seems to present a sensitivity to the way the state-space model is designed. Different radiation force model were compared to achieve the most accurate models possible from the NTNU Toolbox and the Prony’s method. This issue might not be noticed if the model used to evaluated the MPC algorithm is identical to that used to design the control. [SEP] Justify:

Output: The text contrasts the claim by explaining specific conditions under which the MPC and RHPSC control algorithms operate, indicating that their performance is determined under regular and irregular wave constraints rather than in all ocean conditions. The fact that the algorithms are noted to be sensitive to the state-space model design and that their effectiveness may vary based on the wave conditions suggests that wave energy devices do not work efficiently in 'any part of the ocean.' This nuanced view highlights limitations rather than blanket efficiency.


Input: Task: pass the claim using the given text. Claim: Functional MRI can measure brain activity by detecting changes in blood flow. [SEP] Text: CrossrefPubMedWeb of Science26. Neuronal mechanisms of perceptual learning: changes in human brain activity with training in orientation discrimination. Neuroimage 1999; 9: 46–62. CrossrefPubMed27. Chao-Gan Y, Yu-Feng Z. : a toolbox for “Pipeline” data analysis of resting-state fMRI. Neurosci 2010; 4: 13. PubMed28. No abnormalities of intrinsic brain connectivity in the interictal phase of migraine with aura. Neurol 2015; 22: 702–e46. CrossrefPubMedWeb of Science29. . Altered functional magnetic resonance imaging resting-state connectivity in periaqueductal gray networks in migraine. 2011; 70: 838–845. CrossrefPubMedWeb of Science30. The missing link: enhanced functional connectivity between amygdala and visceroceptive cortex in migraine. Cephalalgia 2013; 33: 1264–1268. CrossrefPubMedWeb of Science31. Altered functional connectivity of amygdala underlying the neuromechanism of migraine pathogenesis. J 2017; 18: 7. CrossrefPubMed32. The migraine brain in transition: girls vs boys. Pain 2015; 156: 2212–2221. CrossrefPubMed33. Her versus his migraine: multiple sex differences in brain function and structure. Brain 2012; 135: 2546–2559. CrossrefPubMedWeb of Science34. Left and right amygdala – mediofrontal cortical functional connectivity is differentially modulated by harm avoidance. PloS One 2014; 9: e95740. CrossrefPubMed35. . Cortico-cortical connections of primary sensory areas and associated symptoms in migraine. eNeuro 2017; 3: 6. 36. The sensorimotor network dysfunction in migraineurs without aura: a resting-state fMRI study. J Neurol 2017; 264: 654–663. CrossrefPubMed37. Migraine and cognitive decline among women: prospective cohort study. BMJ 2012; 345: e5027. CrossrefPubMed38. Cognitive function is not impaired in people with a long history of migraine: a blinded study. Cephalalgia 2006; 26: 74–80. CrossrefPubMedWeb of Science39. How migraines impact cognitive function: findings from the Baltimore ECA. [SEP] Justify:

Output: The text does not directly address the claim about functional MRI measuring brain activity through blood flow changes. Instead, it mentions various studies related to migraine and brain connectivity without clearly explaining the mechanics of how functional MRI operates or validating the claim itself.


Input: Task: corroborate the claim using the given text. Claim: Climate change has contributed to more frequent and intense heatwaves. [SEP] Text: Histogram of the JJAS season of TWmax (°C) for each GHG scenario’s ensemble: historical (black), RCP4. 5 (blue), and RCP8. 5 (red). The histogram bin interval is 1. 0 °C and the values on the y-axis indicate the number of exceedances. Values indicated within each plot represent the 50th and 95th percentile event thresholds. TWmax is the maximum daily value from 6-h running average for each day (bias correction described in the Methods section). The figure was created using the ( but the background image was obtained from size imageIrrigation enhances the response of maximum TWmax to climate change. The response of maximum TWmax over irrigated areas due to the combination of irrigation and climate change (estimated from Fig. 2a, f) is about 3. 9 °C. This is larger than the sum of: (i) the response of maximum TWmax over irrigated areas to irrigation alone (estimated from Fig. 2a, d), of about 0. 5 °C, and (ii) the response of maximum TWmax over the same areas (without irrigation) to climate change alone (estimated from Fig. 2a, c) of about 2. 9 °C. This non-linearity in the response of TW to the combination of irrigation and climate change is likely caused by an enhanced water vapor feedback mechanism over irrigated areas. This topic will be investigated in our future research. Comparison of extreme heatwaves over NCP and Asian beltThe impact of climate change over NCP is compared to the corresponding impacts in other locations within the Asian belt that was identified in our recent study17 as a hotspot for extreme heatwaves based on observations from the last few decades (Fig. 4). The response of extreme TWmax to the GHG forcing over NCP is significantly larger than the corresponding response over the region or that over the Ganges and Indus valleys (see 5). In comparison to the NCP is the only region within that Asian belt that is not influenced by a warm ocean or sea next to it. This observation may explain some of the enhanced response in this region. If we use the 95% percentile value of TWmax as a measure of the intensity of extreme heatwaves, this variable increases by about 3 to 4 °C over the NCP, under RCP8. [SEP] Justify:

Output: The text corroborates the claim by detailing the response of maximum daily temperatures (TWmax) to climate change, indicating that under climate change scenarios, the TWmax increases significantly in specific regions like the NCP. It mentions that the response of extreme TWmax increases by about 3 to 4 °C over the NCP under RCP8, which supports the assertion that climate change contributes to more frequent and intense heatwaves.


Input: Task: contrast the claim using the given text. Claim: Wave energy devices work efficiently in any part of the ocean. [SEP] Text: 1 IntroductionMotivated by a desire to decrease global emissions of carbon and other environmental pollutants, the world is moving steadily to replace fossil fuel-based energy supplies with renewable sources. Ocean wave energy has the potential to make a significant contribution to this effort. A detailed description of the resource is given in Barstow et al. (2008). Estimates for the realistically exploitable resource vary, but according to the above cited work it is on the order of 10–25 % of current electricity consumption, while a recent comprehensive study by the in Jacobsen (2011) estimates that in the USA it could be as much as 1/3 of current demand. Some countries have even larger potentials. Wave energy converter (WEC) technology is still relatively young compared to wind, water, solar and even tidal power extraction, and it thus faces significant hurdles to achieving economically competitive designs. Significant political activity has recently been initiated, however, to mature the industry and speed the large-scale deployment of wave energy installations, see for example: IEA (2014), OEE (2014) and SIOcean (2012). The large-scale exploitation of wave energy will most likely progress gradually from breakwater-based and near-shore devices (e. g. . IT 2014; Oyster 2014) to smaller- and finally larger-scale deep water sites. The progression to deeper water will ideally take place together with wind power to exploit the combined use of infrastructure, maintenance services and the shielding effect of the WECs on the wind turbines. Compared to point absorber and terminator WECs, in deep water attenuator devices have high theoretical absorption width and low mooring loads. The low loads are due to the exploitation of internal (non-rigid-body mode) degrees of freedom and a subsequent cancelation of forces along the long ship-like structure oriented parallel to the main direction of wave propagation. The degrees of freedom which are used to extract energy from the waves can be hinged motions (e. g. Pelamis 2014), individual mechanical oscillators (e. g. Wavestar 2014), or Column (OWC) chambers. OWC chambers have the additional attractive feature of having no moving parts in the water, albeit at the expense of a generally reduced Off (PTO) conversion efficiency associated with existing air turbines compared to hydraulic systems. [SEP] Justify:

Output: The text contrasts the claim by indicating that wave energy converter (WEC) technology is still relatively young and faces significant hurdles to achieving economically competitive designs. It implies that while wave energy has potential, the large-scale exploitation will likely start from near-shore and breakwater-based devices, progressing to deeper water over time. This suggests that WECs may not work efficiently in all parts of the ocean initially, particularly deep water, where different technology challenges exist.


Input: Task: pass the claim using the given text. Claim: Functional MRI can measure brain activity by detecting changes in blood flow. [SEP] Text: Neurology 2007; 68: 1417–1424. CrossrefPubMedWeb of Science40. Cognitive dysfunctions and psychological symptoms in migraine without aura: a cross-sectional study. J2016; 17: 76. CrossrefPubMed41. Executive resting-state network connectivity in migraine without aura. Cephalalgia 2012; 32: 1041–1048. CrossrefPubMedWeb of Science42. Abnormal connectivity within executive resting-state network in migraine with aura. Headache 2015; 55: 794–805. CrossrefPubMedWeb of Science43. A default mode of brain function. 2001; 98: 676–682. CrossrefPubMedWeb of Science44. Disrupted default mode network connectivity in migraine without aura. J 2013; 14: 89. CrossrefPubMedWeb of Science45. Ipsilateral alteration of resting state activity suggests that cortical dysfunction contributes to the pathogenesis of cluster headache. 2017; 30: 281–289. CrossrefPubMed46. Regional homogeneity abnormalities in patients with interictal migraine without aura: a resting-state study. NMR Biomed 2012; 25: 806–812. CrossrefPubMedWeb of Science47. Intrinsic brain network abnormalities in migraines without aura revealed in resting-state fMRI. PloS One 2012; 7: e52927. CrossrefPubMedWeb of Science48. Increased default mode network connectivity and increased regional homogeneity in migraineurs without aura. J 2016; 17: 98. CrossrefPubMed49. Functional magnetic resonance imaging in spontaneous attacks of SUNCT: short-lasting neuralgiform headache with conjunctival injection and tearing. 1999; 46: 791–794. CrossrefPubMedWeb of Science50. PET and MRA findings in cluster headache and MRA in experimental pain. Neurology 2000; 55: 1328–1335. CrossrefPubMedWeb of Science51. Posterior hypothalamic and brainstem activation in hemicrania continua. Headache 2004; 44: 747–761. CrossrefPubMedWeb of Science52. SUNCT: bilateral hypothalamic activation during headache attacks and resolving of symptoms after trigeminal decompression. Pain 2005; 113: 422–426. CrossrefPubMedWeb of Science53. Specific hypothalamic activation during a spontaneous cluster headache attack. [SEP] Justify:

Output: The text is irrelevant to the claim as it discusses various studies related to migraines, headache types, and potential brain activity but does not directly address the functionality of fMRI in measuring brain activity or changes in blood flow.