dataesr/scientific-paragraphs-categorization huggingface dataset:
It works as a filter of frequency and only captures the effect of variations over periods the size of the window or larger. We had to find a balance between the length of our dataset and the pertinent length of the trends we wanted to analyse. Since we want to look at the effects of climate change, we do not need to capture the high inter-annual variability and can focus on decadal trends or longer. Furthermore, a shorter time window would not be adapted to the hypothesis to the Budyko framework which needs a long enough time-period to be fitted. So the window can't be shorter  (Tian et al. , 2018). We could test longer timewindow to test which is the limit time-length capturing the most impacting effect on discharge. However the longer the time-window, the fewer the points we will have to evaluate the trends.
Therefore we could not decompose the effects of P ET as easily as for the effects of P , which would be interesting to do especially in the areas where P is less limiting such as in the West of France or in the North of Europe. The amplitude of the results could also depend on the choice of the model or of the forcing data. We tested the use of other forcing datasets: WFDEI  (Weedon et al. , 2014) , which covers the time period from 1979 to 2010, with the same resolution than GSWP3 and E2OFD  (Beck et al. , 2017) , also covering 1979 to 2010 but at a lower resolution. We also tested the use of another model with another forcing, the model SURFEX  (Quintana-Seguí et al. , 2020)  forced with  SAFRAN (Quintana-Seguí et al. , 2017) , over the Ebro river. It gave very similar results over the overlapping period, with little differences in the sig-nificance of the trends and their amplitude, showing that the results mostly are dependent on the resolution of the forcing rather than on the forcing or the model used.
Open Research Data availability statement The forcing dataset GSWP3 used to grid P and other climate data and run the LSM over Europe between 1901 and 2010 in the study is attributed to  Hyungjun (2017). It is available upon request via doi:10. 20783/DIAS. 501. The Land Surface Model used to calculate E0 and model the discharge in this study is the LSM Organizing Carbon and Hydrology In Dynamic Ecosystems (ORCHIDEE) from the Institut Pierre Simon Laplace (IPSL). The Main version used in this study is available upon request on the official website https://orchidee. ipsl. fr/. It relies on the hydrological elevation model HydroSHEDS  (Lehner et al. , 2008)  to construct its routing graphs for rivers and reconstruct the upstream areas of gauging stations placed on its grid. It is available to download from https://www.
hydrosheds. org/. The gauging stations positioned on the grid come from a database composed from several sources. The main dataset is the one from the Global Runoff Data Centre (GRDC). It is available to download from https://www. bafg. de/GRDC/EN/02 srvcs/21 tmsrs/ riverdischarge node. html. It was completed over Spain with data obtained from the Geoportal of Spain Ministerio (Ministerio de Agricultura, pesca y alimentacion, Ministerio para la transicion ecologica y el reto demografico, 2020) and over France with data from the database HYDRO (Ministere de l'ecologie, du developpement durable et de l'energie) (available on request to https://www. hydro. eaufrance. fr/, downloaded in February 2021). In this study, we only used the position of these stations and the associated upstream area to reconstruct meaningful watersheds.
PlioMIP2 models For this study, complete datasets for analysis are available from 15 out of 17 models participating in PlioMIP2. The models are listed in Table  1  along with their institute and reference to the individual model description. All models have performed a pre-industrial (E 280 ) simulation at a CO 2 concentration of 280 ppm (models participating in CMIP6 at 284. 3 ppm, see Table  1 ) and a mid-Pliocene (Eoi 400 ) simulation at a CO 2 concentration of 400 ppm and with boundary conditions implemented as described in  Haywood et al. (2016). The exception is HadGEM3, where the pre-industrial land-sea mask and bathymetry is also used in the mid-Pliocene simulation  (Williams et al. , 2021). For this reason, the model is excluded from any multi-model  2 Pre-industrial land-sea mask in both simulations.
Proxy data To compare mid-Pliocene model results with reconstructed data, we use SST proxy data from a 30k year interval on the KM5c time slice by  Foley and Dowsett (2019)  and  McClymont et al. (2020). The PRISM4 dataset  (Foley and Dowsett, 2019)   37 proxies (calibrated using the BAYSPLINE method) as well as foraminifera Mg/Ca proxy SST reconstructions. In this study, SST reconstructions from six different sites between 30 N and 70 N in the North Atlantic have been used. For comparison to model anomalies, the NOAA ERSST5 dataset  (Huang et al. , 2017)  for the years 1870-1899 is used as the observational pre-industrial SST dataset.
Acknowledgements. The work by  Julia E. Weiffenbach ,  Michiel L. J. Baatsen ,  Henk A. Dijkstra  and  Anna S. von der Heydt  was carried out under the program of the  Netherlands Earth System Science Centre (NESSC) , financially supported by the  Ministry of Education, Culture and Science (OCW  grant number  024. 002. 001 )). CCSM4-Utr simulations were performed at the SURFsara Dutch national computing facilities.
Code and data availability. PlioMIP2 data used for this paper is available upon request from Alan M. Haywood (a. m. haywood@leeds. ac. uk), with the exception of IPSL-CM6A, EC-Earth3-LR and GISS2. 1G. PlioMIP2 data from IPSL-CM6A, EC-Earth3-LR and GISS2. 1G can be obtained from the Earth System Grid Federation (ESGF) (https://esgf-node. llnl. gov/search/cmip6/, last access: 14 February 2022). The U k 0 37 and Mg/Ca SST reconstructions from McClymont et al. (2020)  can be obtained through https://doi. pangaea. de/10. 1594/PANGAEA. 911847 (last access: 24 January 2022) and the U k 0 37 SST reconstructions from  Foley and Dowsett (2019)  can be obtained through https://doi. org/10. 5066/P9YP3DTV (last access: 24 January 2022). The observational pre-industrial SSTs from the NOAA ERSST5 dataset  (Huang et al.
MATERIALS AND METHODS Up to date, literature were sought from MEDLINE via PubMed, as well as relevant international guidelines, to inform the REVIEW Hypertension. 2022;79:12-23. DOI: 10. 1161/HYPERTENSIONAHA. 121. 17596 January 2022 13   Lane et al Guidance for Hypertension Adherence Testing recommendations made in this article. Subsequent iterations of the article were reviewed in regular meetings of the authors to reach the consensus on the usefulness and weight of the recommendations. Details of how data were extracted from the medication dispensary databases are outlined in the Supplemental Material (Methods section). Prescription rate data is available on request from the authors.
SAMPLE REQUIREMENTS Currently, there is no data on the optimal time of sample collection and further research is needed in this area. Therefore, for pragmatic reasons, we recommend collecting samples when the patient attends a clinic. The minimum amount needed for urine analysis is small (<1. 5 mL), although collection should be done in <20 mL standard universal containers. For blood samples, at least 1 mL should be collected in lithium heparin or, alternatively, an EDTA tube may be used for the collection of plasma. Serum may also be collected, as well as dried blood spot sampling (where the facilities for analysis of such samples are available).
Observational studies have shown that chemical testing followed by discussion of results with a nonadherent patient, improves adherence status and BP. 5  Further evidence in the form of randomized control trials is needed as long-term outcomes after chemical adherence testing are poorly researched. The OUTREACH trial (https:// www. clinicaltrials. gov; Unique identifier: NCT03293147) is aimed to answer this question. Studies have shown that there are not only reasonable congruities but also differences between detection of medication in urine versus blood. 79, 80 Larger studies are needed to study correlation between nonadherence results based on urine and blood samples, but if there is a good correlation between blood and urine chemical nonadherence results, this would not necessarily mean that these can be used interchangeably.
In a context of present-day GIS melting, several studies suggest that the inflow of fresh water into the northern part of the North Atlantic Ocean may weaken the AMOC  (Böning et al. , 2016; Driesschaert et al. , 2007). Our results suggest that it might also be relevant to take into account the effects of changing ice-sheet elevation and albedo induced by melting, as these induce changes in atmosphere circulation that slow down the AMOC. In contrast, the closure of the Eastern Tethys Seaway that occurred during the Miocene does not have a significant impact on the intensity of NADW formation. reviewers for their relevant comments and suggestions that clearly improved the manuscript. We thank the CEA/ CCRT for providing access to the HPC resources of TGCC under the allocation 2019-A0050102212 and 2020-A0090102212 made by GENCI and French ANR projects AMOR (YD, ANR-16-CE31-0020) and MioCarb (BSM, ANR-20-CE49-0002) for providing funding for this work.
Data Availability Statement LMDZ, XIOS, NEMO and ORCHIDEE are released under the terms of the CeCILL license. OASIS-MCT is released under the terms of the Lesser GNU General Public License (LGPL). IPSL-CM5A2 source code is publicly available through  (Pillot, 2022b). The mod. def file provides information regarding the different revisions used, namely: • NEMOGCM branch nemo_v3_6_STABLE revision 6665 • XIOS2 branchs/xios-2. 5 revision 1763 • IOIPSL/src svn tags/v2_2_2 • LMDZ5 branches/IPSLCM5A2. 1 rev 3591 • branches/publications/ORCHIDEE IPSLCM5A2. 1. r5307 rev 6336 • OASIS3-MCT 2. 0 branch (rev 4775 IPSL server).
The sea surface physique nature is an important parameter in determining the strength of the scattered signals back to the radar. It is always changing, and its complex nature implies that the sea clutter RCS fluctuates widely around the mean value as determined by the NRCS σ 0 , which has been the subject of numerous calculations, depending on the sea state, the radar look direction, wavelength, and polarization. In this sense, researchers have published several empirical models. Examples of such models include the TSC (Technology Service Corporation, Arlington, VA, USA) model  [39] , the GIT (Georgia Institute of Technology, Atlanta, GA, USA) model  [23] , Hybrid model  [40] , NRL model  [41] , and a model developed by the Royal Radar Establishment (RRE) in United Kingdom  [24]. However, these models do not always agree with the most complete experimental database of sea clutter reflectivity.
The present study explores the impact of anthropogenic forcings (ANT) on surface air temperatures (SATs) across East Asia (EA) over a long period (1850-2014) using the new Coupled Model Intercomparison Project Phase 6 (CMIP6) datasets. Based on CMIP6 multi-model ensemble simulations, the historical simulations (twentieth century) and future (twenty-first century) SAT projections were investigated. Our calculations show that during 1850-2014, the combination of ANT and natural (NAT) ('ALL = ANT + NAT') forcings increased the EA's SAT by 0. 031 °C/decade, while a high increase of 0. 08 °C/decade due to greenhouse gas (GHG) emissions. The ANT forcing rapidly increased after 1969. As a result, SAT change was enhanced at a rate of 0. 268 °C/decade and 0. 255 °C/decade due to GHG and ALL forcings, respectively.
05). In addition, we also evaluated the spatial SAT trends for both CMIP6 (ALL forcing) and CRU datasets; these are shown in Fig. S1. The CMIP6 trends more closely match the CRU trends with a bias of less than ± 1. These trends show that northern EA experienced the highest warming trend, while southeastern China showed the lowest warming for the 1901-2014 period. We calculated the spatial distributions of the annual mean SAT MME mean response trends (based on CMIP6) to the different forcings for the period 1850-2014. Figure  2a -e correspond to the temperature trends associated with the simulations of historical (ALL), hist-aer (AER), hist-nat (NAT), hist-GHG (GHG), and histsol (SOL) forcings. The trends are estimated based on robust regression fitting analysis. The lowest increases in temperature owing to ALL and GHG occurred in southeast China, while the highest increases were observed in Tibet and southern Mongolia.
However, we used SSP2-4. 5 scenarios from 2015 to 2019 to match the duration of ALL forcing simulations to the other forcing simulations. Figure  3  shows the temperature anomaly response to ALL forcing as well as ground observations, CRU (1901-2018), and HadCRUT4 (1850-2019) for all of EA and each country. Based on these results, the SAT trends associated with ALL forcings were consistent with the observed trends. The SATs in the HadCRUT4 and ALL datasets were mostly stable between 1850 and 1900, after which SAT dropped. This may have been caused by the cooling effect of AER. From 1901 to 1970, the observations (CRU and HadCRUT4) and the ALL data anomalies show slow increasing SAT trends. However, from 1970 to 2014/2019, SAT increased rapidly owing to the third industrial revolution. Across all of EA, SAT had increased by 1.
5 °C by 2019, with trends in each country being broadly similar. Specifically, under the ALL forcings, all the East Asian countries experienced smaller increments of change between 1900 and 1969 compared to a higher rate of increase between 1970 and 2014. By 2019, the SATs in China and Japan had increased by ≥ 1 °C, while in Korea and Mongolia, the increase had reached ≥ 1. 5 °C. The SAT anomaly response to GHG forcing is shown in Fig. 4  for the period 1850-2019, with observed and simulated SAT datasets showing a similar increasing trend. A slow increase in GHG forcing is observed until 1969, after which SATs begin to increase very rapidly. Indeed, GHG forcing has been the dominant contributor to the observed SAT warming in each country as well as across the whole of EA. For example, GHG forcing increased SATs by approximately 1.
Methods Data. For the long-term variability analysis of SAT, we used the CRU time series version 4. 03 (resolution = 0. 5° × 0. 5°) and HadCRUT4 (resolution = 5° × 5°) observational datasets for the period 1901-2018 and 1850-2019, respectively; the HadCRUT4 dataset (available at https:// cruda ta. uea. ac. uk/ cru/ data/ tempe rature/) was primarily used to cover the period not considered by the CRU dataset (1850-1900). The CRU data are derived from the spatial interpolation of monthly climate anomalies of worldwide weather station observations  [26] [27] [28]  and are available at https:// cruda ta. uea. ac. uk/ cru/ data/ hrg/. These CRU data have been used by many researchers studying long-term temperature and precipitation changes over the EA region  26, 27, 29, 30  and they correlate well with ground station datasets.
The land and ocean data were separated, and the former was employed in this study. We used geographical data available at https:// www. geoda tasou rce. com/ addon/ count ry-borde rs to identify the country borders (EA, China, Mongolia, Japan, and Korea), and the data within those borders were used in this study. The state-of-the-art global multimodel simulations of CMIP6 www. nature. com/scientificreports/ llnl. gov/ proje cts/ cmip6/. Some of the CMIP6 models are more biased when compared to observational datasets  32, 33. It is known that the various models have different boundary and initial conditions, parameter values, and structural uncertainties in the model design. Weather changes or predictions are sensitive to small difference/variation in the initial conditions of the models. Further, some models may have higher resolutions and others have lower resolutions, and accordingly, inter-model variability exists in terms of the different forcings that influence the climatology of the atmosphere.
The low SSP1-2. 6 and SSP2-4. 5 scenarios and high SSP5-8. 5 scenarios were used for the future projections (2015-2100). Initially, the monthly anomalies of the historical (considered land data and discarded ocean data) datasets (ALL, AER, NAT, GHG, and SOL) and future scenarios (SSP1-2. 6, SSP2-4. 5, and SSP5-8. 5) were collected from CMIP6. The simulations driven with ALL from 21 models, had 91 runs. The simulations driven by the other forcings of AER, NAT, GHG, and SOL included 10 (46), 10 (48), 11 (46), and 4 (  22 ) models (runs), respectively (see Table  1 ). Similarly, the future projections driven by SSP1-2. 6, SSP2-4. 5, and SSP5-8. 5 included an equal number of 8 (  37 ) models (runs), respectively (see Table  S2 ). The 1° × 1° gridded data were averaged for their respective models.
The influence of the different forcings was detected based on β. A 90% confidence interval of β greater than zero implies that a corresponding influence of external forcing has been detected. If the 90% range of β is above zero and it includes unity, this indicates that the observed change is consistent with the model simulations. If the 90% interval of β is greater than unity, this implies that the observed changes are underestimated by the model simulations. For further details regarding the ROF method, refer to Ribes et al. 40  and Ribes and Terray  39. For detection and attribution, we used the MME means of the available simulations for ALL, GHG, ANT, and NAT, which largely smoothed the uncorrelated internal variations  51. ANT was estimated by subtracting NAT from ALL (ANT = ALL -NAT) and CRU data used as the observational data.
We conducted one-signal, twosignal, and three-signal analyses over the last hundred years (1905-2014) for each EA country and EA overall. Detection and attribution are most effective at small data dimensions, allowing better estimation of climate response and, thus, are usually conducted within shorter dimensions  14. Therefore, we converted the annual data sets into non-overlapping ten-year means for the 1905-2014 period, which produced 11 data values (1905-1914,  1915-1924, 1925-1934, … and 2005-2014). The use of 10-year temporal means reduced the time dimension, as well as the variability of the observations and noise in the climate signals  25, 51. In the one-signal analysis (Eq. 1), the observations were regressed onto MME mean responses of ALL and ANT fingerprints separately to detect their relative influence on the observed change.
Acknowledgements This work was supported by the  KICT   Research Program  ( 20220136-001 ,  Development of 3D Fine Dust Estimating Technology Based on AI Image Analysis ) funded by the  Ministry of Science and ICT. We acknowledge the  World Climate Research Program  for making the CMIP6 dataset available for global-and regional-scale climate research. The authors would like to thank the  National Climate Center, Research , for providing the CRU dataset.
Data availability The datasets used in the present study are: HadCRUT4, available at https:// cruda ta. uea. ac. uk/ cru/ data/ tempe rature/; CRU, available at https:// cruda ta. uea. ac. uk/ cru/ data/ hrg/; and CMIP6, available at https:// esgf-node. llnl. gov/ proje cts/ cmip6/.
e. , the capacity to detect and respond to emotional states) so that it might provide effective psychosocial and health and wellbeing support to astronauts (https://www. technologyreview. com/2020/01/14/64990/an-emotionally-intelligent-ai-couldsupport-astronauts-on-a-trip-to-mars/). Similarly, the Translational Research Institute for Space Health (TRISH) has recently funded a project by Ejenta, which aims to develop a conversational artificial intelligence (AI) that could provide emotional support to crew members during LDSE missions. Like CIMON, this work draws upon advances in natural language processing, speech recognition, and machine learning. Although these projects are in their infancy and may be some way off from being realised in the ways imagined, the trend toward using real-time monitoring data to drive smart, personalized behavioural and mental health solutions is likely to continue.
Recent critiques of AI systems have highlighted the importance of having robust and representative datasets to avoid biased outcomes that result from biased training datasets. Identifying such datasets for well-being support in space is a particular challenge. Given the small numbers of individuals who experience LEO spaceflight (let alone LDSE), a representative dataset of any significant size will be challenging to collect. Using an unrepresentative dataset could result in irrelevant or erroneous recommendations to astronauts that might significantly affect behaviour, health, and performance outcomes. One possible way to mitigate the risk of an unrepresentative dataset is by using an initial training dataset collected in analogous settings, such as exploration in Polar or other extreme environment settings, commonly viewed as spaceflight analogues.
https://doi. org/10. 5194/essd-2021-444 Throughout all deployments, a still image camera, a high-resolution video camera, a forward-facing acoustic camera and a multibeam side scan system collected data concurrently from the seafloor. Each of these systems are introduced in more detail in subsections 2. 1. 1 -2. 1. 4. To allow visual seafloor coverage estimations to be made for the areas covered by each image or video frame, three red sizing lasers (FLEXPOINT) were mounted vertically on the OFOBS frame, pointing perpendicularly, and with a 50 cm spacing  (Purser et al. , 2019b). For the majority of deployments during PS124, 4-6 m 2 of seafloor was imaged in each photograph. The OFOBS was generally towed at a speed of 0. 5 kts at a height of 1. 5 -2 m above the seafloor. For Stations PS124_54-1, PS124_63-1, PS124_67-1 and PS124_101-1, seafloor coverage per image was 15-25 m 2 , as for these deployments a higher flight altitude of 2.
Still image data collection Still images were collected with a 26 megapixel still camera system (iSiTEC, Canon EOS 5D Mark 3). Images were collected at automated 20 second intervals throughout each deployment, though additional 'hotkey' images could be taken to record features of interest, such as the first observations of particular fauna, items of litter or geological features. Within the data set, images taken by the timer were designated with the prefix 'TIMER', whereas those taken manually by the OFOBS operations team were designated with the prefix 'SW_RELEASER' or 'HOTKEY'. No pre-processing stages were applied to the maximum resolution. jpeg data collected from the camera, which are provided here at maximum acquired resolution. The data collected during PS124 complements data collected from the same or stations in close proximity during Antarctic cruises over the last two decades, as a time series survey (i.
Video image data collection Throughout all deployments, HD (high-definition) video data were recorded by the OFOBS for the duration of each dive with an HD video recorder (iSiTEC, Sony FCB-H11). Unfortunately, during PS124 problems with communication with the camera resulted in fragmented video files and occasional gaps in video data collection. Despite the breakup in the video data, the collected fragments may be used for the 3D 'Structure from Motion' reconstruction of areas of high resolution seafloor topography, an approach which has recently been used with OFOBS data to demonstrate the movement of Arctic sponges  (Morganti et al. , 2021)  and the interactions of deep diving whales with the deep sea seafloor  (Purser et al. , 2019a).