dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgements The authors would like to acknowledge that this work was conducted on ancestorial lands of native and indigenous peoples. Field work was conducted on the land of the M ā'ohi people (Moorea, French Polynesia). Laboratory work was conducted on the original lands of the Anishinaabeg, Cree,  Oji-Cree , Dakota, Dene peoples, and on the homeland of the  Métis Nation (Winnipeg, Manitoba ). Additionally, the authors would like to acknowledge the staff at CRIOBE for.
, 2018). Its implementation (see Appendix A) preserves the horizontal continuity of the fields without the blocking effect of staircase coordinates. The bathymetry is built from the GEBCO database. Fig. 2  shows the bathymetry of the Eastern Mediterranean as specified in the model. The names of geographical landmarks specified in the text are also indicated. The model is initialized from an analysis produced by the operational oceanography center MERCATOR OCEAN International, MOI  (Lellouche et al. , 2013). As stratification is crucial for mesoscale characteristics, the stratification has been debiased from observations collected over the whole basin as in  Estournel et al. (2016)  while preserving the first hundred meters which benefits optimally from the data assimilation performed at MOI.
For the deeper extinction which concerns 42% of the solar flux, we averaged over 3 years the monthly averages of the OLCI Diffuse Attenuation Coefficient at 490 nm (product Copernicus Marine Service: OCEAN-COLOUR_MED_OPTICS_L4_NRT_OBSERVATIONS_009_039) then we interpolated this field on the model grid. These data reflect the wellknown behavior of the water transparency in the Mediterranean which increases from west to east and is reduced in coastal areas. After some tests and comparisons with temperature profiles in the Gulf of Lion and in the Levantine Basin, we produced an adjustment of these values according to Eq. (  2 ) which, moreover, specifies minimum and maximum values of 10 and 30 m for the depth used in the exponential attenuation law: Kd -1 model = Max ( Min ( 0. 9 × ( Kd -1 sat -2 ) , 30 ) , 10 ) (2) Fig.
In winter, these two water masses are mixed. In the Adriatic, apart from the bias of the intermediate layers already noted (below 100 m depth), the seasonality of the stratification is also well reproduced by the model with, apart from winter, the low surface salinities linked to the fluvial inputs and the tendency to mix in winter. In the South Aegean, the model-observation agreement is less good in the stratified period, while the winter profile is well represented. As pointed out above, spatial variability is important in this region. Moreover, the number of Argo profiles is relatively small, making the identification of a mean seasonal variability a difficult issue from the Argo database at the scale of this region. Looking more specifically at this zone, we see that the overestimation is located over a 2-year period starting in the summer of 2017, during which the observed salinity decreases, also imprinting the salinity curve of the whole basin over 2018 (Fig.
Conclusion The 9-year simulation presented in this paper has been compared to surface observations (SST), and from 20 to 800 m, to the huge Argo database which counts over this period about 25,000 profiles in the Eastern Mediterranean. Generally the biases are low and the correlations are good over the salinity and temperature fields at different depths while the mean vertical profiles in each water mass formation sub-basin are also well represented. To our knowledge, this is the first time a model has been evaluated in such detail. The horizontal resolution of 3-4 km, and probably also a wind with relatively high horizontal resolution, make it possible to represent the mesoscale as attested by the recurrent and faithful representation of many eddies well identified in the literature as well as fine SST structures.
Acknowledgements The authors warmly acknowledge the international  ARGO program  for supporting the deployment of profilers. Argo data were collected and made freely available by the  CORIOLIS  project (http://www. coriolis. eu. org) and programs that contribute to it. The outputs of the operational oceanic model used for the boundary conditions were provided by  Mercator-Ocean International. The atmospheric fields were provided by  ECMWF. We thank  Françoise Orain  from  Météo-France  for her help with the SST images. Rivers discharge were downloaded from  Settore Idrologico e Geologico Regionale from Toscane Region for Arno, ARPAE (Agenzio Prevenzione Ambiante Energia Emilia Romagna ) for Po, SAIH-Ebro for Ebro,  Banque Hydro  for French rivers. Numerical simulations were performed using HPC resources from CALMIP (grants  P09115  and  P1325 ) and from  GENCI  and  CINES (Grand Equipement National de Calcul Intensif , project  A0040110088 ).
Activity Activity concentrations of 134 Cs and 137 Cs in dissolved form and all particles collected at 12 stations off Tomioka on 28 August 2014 used in this study are in a published dataset entitled "Dataset of 134Cs and 137Cs activity concentrations in dissolved form and, all particles at Tomioka, Fukushima in August 2014" as doi: 10. 34355/CRiED. U. TSUKUBA. 00031 (Aoyama et al. , 2020b). Activity concentrations of 134 Cs and 137 Cs in dissolved form collected at Tomioka port during the period from 10 June 2014 to 24 April 2019 used in this study are in a published dataset entitled "Dataset of time series of radiocaesium activity concentrations at Tomioka, Fukushima during the period from 10 June 2014 to 24 April 2019" as doi: 10. 34355/CRiED. U. TSUKUBA. 00032 (Aoyama et al. , 2020c). We also used TEPCO time series monitoring data of 137 Cs activity concentration in surface water at Ukedo port, near the outlet of canal from unit 5 and 6 of FNPP1 to the sea, hereafter 56N of FNPP1, and Iwasawa beach (open circles in Fig.
Dissolved radiocaesium The detection of both 134 Cs and 137 Cs in all samples confirmed contamination from the FNPP1 accident. Generally, the dissolved concentrations were higher at coastal sites and decreased with distance from the coast (Fig. 2 ), and they were higher in the surface layer compared to the bottom layer (Fig. 3 ). At the stations very close to the coast within 1 km from the coast, 137 Cs activity concentration ranged from 10. 6 ± 0. 6 Bq m -3 at the bottom of Niida 1 station to 62. 5 ± 3. 5 Bq m -3 at the surface of station S12 as shown in Fig. 3  and in a dataset doi: 10. 34355/CRiED. U. TSUKUBA. 00030 (Aoyama et al. , 2020a). They then decreased to less than 10 Bq m -3 at the stations about 2 km from the coast for bottom samples and at the stations about 4 km from the coast for surface samples along each radial transect (Fig.
3 ). In general, the lowest 137 Cs activity concentration was observed at the bottom at the stations furthest from the coast (Fig. 3 ). The lowest 137 Cs activity concentration was therefore observed at the bottom at the Nitta 5 station (2. 0 ± 0. 1 Bq m -3 ), whereas the highest 137 Cs activity concentration was observed at the surface at station S12 (62. 5 ± 3. 5 Bq m -3 ) as shown in the dataset doi: 10. 34355/CRiED. U. TSUKUBA. 00030 (Aoyama et al. , 2020a). The decreases of 137 Cs activities in surface water with distance from shore were similar along four of the radial transects (the Mano, Niida, Odata, and Ota transects). The activity concentration at a distance of 1 km decreased to 90-96% of that at the first station along each transect and 36-68% of the corresponding activity concentration at a distance of 10 km.
The rate of decrease was higher along the Ukedo transect: 85% at 1 km and 19% at 10 km. These differences might reflect differences in the magnitudes of the river fluxes associated with the heavy rains caused by the typhoons that passed over the study area and the initial 137 Cs activities in the river water. The entire dataset showed clearly higher activities at all the locations nearest the coast, regardless of salinity. In general, 137 Cs activities tended to decrease with distance from the coast. The 137 Cs activities also tended to decrease with increasing salinity (Fig. 4 ) because of mixing of coastal water and open-ocean water in which the 137 Cs activity concentration was ~1. 5 Bq m -3 (see section 4. 2).
concentrations of 134 Cs and 137 Cs in dissolved form, all particles, and organic particles collected during the SoSo 5 Rivers cruise activities used in this study are in a published dataset entitled "Dataset of 134Cs and 137Cs activity concentration https://doi. org/10. 5194/bg-2020-491 Preprint. Discussion started: 8 February 2021 c Author(s) 2021. CC BY 4. 0 License.
Acknowledgments This work was part of the  AMORAD  project ( French State  financial support managed by the  National Agency for Research  allocated in the " Investments for the Future" framework programme as  project number  ANR-11-RSNR-0002 ). The authors thank  Mireille Arnaud  (IRSn) and  Hervé Thébault  ( IRSN ) for their helpful and expert assistance in field work. The authors also thank the  KANSO  team as well as  Franck Giner  ( IRSN ) for their helpful and expert assistance in field work. This work was also a part of a project founded by  IER, Fukushima Univ. to Michio AOYAMA.
Data availability Activity concentrations of 134 Cs and 137 Cs in dissolved form, all particles, and organic particles collected during the SoSo 5 Rivers cruise activities used in this study are in a published dataset entitled "Dataset of 134Cs and 137Cs activity concentration concentrations in dissolved for, all particles and organic form of particles obtained by SoSo 5 rivers cruise in 2014" as doi: 10. 34355/CRiED. U. TSUKUBA. 00030 (Aoyama et al. , 2020a).
| Literature review To investigate the ways in which the topic has already been defined within different research and lay domains, we undertook separate searches using the following five databases: EMBASE, Web of Science, Google Scholar, Medline and PsycINFO, as well as the grey literature. We included peer-reviewed journal articles, reviews, as well as they grey literature, supported by a medical librarian. The span of years from 1990-2020 was chosen as reflective of the start of the 'decade of the brain' (US Congress statement) until the current point. The search terms were: 'brain health' or 'definition' or 'definitions or 'define' or 'defined' or 'defining' or 'consensus' or 'statement' or 'explanation' or 'meaning' or 'exposition' or 'guideline' or 'guidelines' or 'recommendations' or 'view' or 'viewpoint'.
ACKNOWLEDGEMENTS The authors would like to thank the fellows, senior fellows, staff and faculty and regional mentors of the  Global Brain Health Institute  (www. gbhi. org)for supporting this initiative and responding so generously to our survey request. We extend particular thanks to  Dr David Mockler , medical librarian,  St James' Hospital, Dublin. Yaohua Chen  and  Naiara Demnitz  were  Atlantic Fellows for Equity in Brain Health at the Global Brain Health Institute (GBHI ) and were supported with fellowship funding from  GBHI  from  2019  to 2020. The funders had no role in study design, decision to publish or preparation of the manuscript.
Empirical data from humans show how increasing track tortuosity reduces the minimum cost of transport speed to a slow walk We examined the energetics of moving in humans by measuring oxygen consumption in 20 subjects walking defined trajectories consisting of varying angles between straight-line sections at defined speeds. We noted how power increased disproportionately with increasing angular velocity, which resulted in clear cost of transport minima that did not occur at the higher walking speeds. Rather, minimum costs of transport occurred at lower walking speeds when angular velocities were high. Our calculations of power-use by participants walking variously angled courses showed a significant interaction effect between walking speed and turn angle on total mass-specific metabolic power (speed 2 x angle category, F = 101.
Prior to each trial, reference gases were used to calibrate the O2 and CO2 analysers and the turbine volume transducer was calibrated using a 3 L syringe (Hans Rudolph, Kansas City, MO). Respiratory exchange ratios (VCO2:VO2) and their thermal equivalents taken from  (36)  were used to convert VO2 to metabolic power ( , W•kg -1 ). The cost of transport (J•kg -1 •m -1 ) was calculated by dividing by average travel speed (m s -1 ). Identifying angular velocity from turns. All participants were equipped with a lower back, mid-line mounted 'Daily Diary' tag  (37)  recording tri-axial (orthogonal) acceleration and magnetic field intensity at 40 Hz. Post-experiment, all magnetic field data were plotted on a tri-axial spherical plot of magnetic field intensity (m-sphere; see  (38)  for details). While participants walking in straight-lines led to little variation in tri-axial plotted position on the msphere, those that executed turns resulted in rapid movement of points across the m-sphere surface which clearly showed at which points turns began and ended so that turn duration could be derived.
Tags were left in place for between 15 and >250 days before being retrieved. Downloaded data were treated to determine movement patterns using dead-reckoning as described in  (29) , correcting for drift using the periodic GPS positions  (29). From these data, a random period (24-50h) was isolated from each individual, with some variation in durations to ensure that appreciable movement had taken place. For these periods, the vectorial dynamic body acceleration (VeDBA), a good proxy for speed  (23, 39) , was calculated according to  (40)  as well as the angular velocity over one second at approximately 10 s intervals so as to give ca. 20,000 angular velocity-VeDBA pairs per individual. Data from five individuals for all species were used in this manner except for the mouflon, where only 4 animals were used.
Acknowledgements RPW conceived the overall study while KAR, RSM developed and executed the human-based work with support from MAM and KAM. DMS, NM,  NCB , and RPW conceived specific ideas for the lion work and JR, RG, LB, AL, JM, MSP, VS, MG, CT, PM conceived methodologies and implemented various aspects of the fieldwork for the other species. DMS, NM and NCB acquired funding and support. KAR, RSM, JR, RG and RPW performed the analyses while MDH developed and provided software and analytical ideas. All authors contributed to the manuscript preparation.
The prepared biocomposite (MPCB) was characterized using FTIR, SEM, EDX and XRD analysis. Batch experiments were conducted to investigate the adsorption behavior of MPCB gel beads towards Cr(VI) and Cu(II) in single and binary system. The effect of several parameters such as initial metal concentration, pH and contact time were also explored. The adsorption kinetic and isotherm were studied. The competitive sorption and mechanism of metal ions onto biocomposite beads were analyzed. Multicomponent sorption models were employed to analyze the equilibrium data from binary system. Finally, for a better understanding the adsorption mechanism of both ions, an advanced modeling based on physical model was applied to further explained the adsorption process.
Binary systemCr(VI)-Cu(II) C e,Cr(VI) (mg L -1 ) q e,Cr(VI) (mg g -1 ) Rq Cr(VI) Binary systemCu(II)-Cr(VI) C e,Cu(II) (mg L -1 ) q e,Cu(II) (mg g R L = 1 1 + K L c 0 ( 12 ) where K L is the Langmuir constant and C 0 is the initial metal concentration (mg L -1 ). Freundlich isotherm is an empirical model based on heterogeneous multi-layer adsorption  [65]. The Freundlich model is given in equation  (13) : q e = K F C 1/n e (13) where K F is the Freundlich constant related to adsorption capacity and n is the adsorption intensity. The effect of initial concentration on Cu(II) and Cr(VI) sorption onto MPCB was investigated over the range of 10-2000 mg L -1 and 10-3500 mg L -1 respectively, at an optimum pH and time and the equilibrium data was fitted to Langmuir and Freundlich isotherms. The results are exhibited in Fig.
8b ). These results assume that competitive Langmuir isotherm model is suitable for describing the adsorption behavior of Cu (II) in binary metal system however, this model provides a poor fit for Cr (VI) experimental data in binary mixture. To further examine the model performance, the predicted q e values were plotted versus the experimental q e values of Cu(II) and Cr(VI) in the binary system (Fig. 10a, b  and c ). The q e values were distributed around the square diagonal, the good fitting of the model is suggested by a near zero intercept and a close unity slope. The coefficient correlation R 2 for copper and total metal uptake were higher than 0. 9, confirming that the competitive Langmuir model demonstrates a good fit to the equilibrium data of Cu(II) from the binary mixture. However, R 2 value for Cr(VI) was lower than 0.
We describe a new parameterisation that can be applied across the whole spectrum, allows for a wide range of zenith angles in support of solar radiation and geostationary sensors, and for which it will be easier to add new variable gases in support of user requirements. Comparisons against line-by-line radiative transfer simulations, and against observations in the ECMWF operational system yield promising results, suggesting that the new parameterisation generally compares well with the old one in terms of accuracy. Further validation is planned, including testing in operational numerical weather prediction data assimilation systems.
The LBL transmittances are computed at a spectral resolution of 0. 01 cm -1 in the visible/near-IR (2000-25500 cm -1 ) and at a resolution of 0. 001 cm -1 in the IR (75-3325 cm -1 ). For non-hyperspectral sensors, these high resolution transmittances are averaged at 1. 0 cm -1 (visible/near-IR) or 0. 1 cm -1 (IR) before being integrated over the channel spectral response functions in order to calculate the regression coefficients. For hyperspectral IR sounders, the LBL transmittances at 0. 001 cm -1 are integrated over the channel spectral response functions directly. At the time of writing, the latest RTTOV coefficients available for visible/IR sensors are trained using LBLRTM v12. 8 with the AER v3. 6 molecular database and MT-CKD_3. 2 for continuum absorption. For microwave sensors the AMSUTRAN model based on MPM89 (Liebe 1989) is used, and is described fully in  Turner et al.
(2019). In contrast to the visible/IR case, the microwave LBL transmittance calculations are performed during the coefficient generation process with no requirement for pre-computed transmittance databases. RTTOV predicts optical depths due to water vapour and, optionally, a selection of other trace gases: ozone (O 3 ), carbon dioxide (CO 2 ), nitrous oxide (N 2 O), carbon monoxide (CO), methane (CH 4 ), and sulphur dioxide (SO 2 ). To account for the absorption by other radiatively significant gases, coefficients are also computed to obtain the optical depth due to "mixed gases" which comprise fixed climatological profiles of relevant gases that subsequently cannot be varied in the RTTOV simulations. In the existing parameterisation, RTTOV implements three sets of predictors, named after the RTTOV versions in which they were introduced.
Finally, where any individual predicted gas layer optical depth is less than zero, it is set to zero before the correction term regression is computed. Similarly, where the predicted total layer optical depth (including the correction term) is less than zero, this is also set to zero. The new parameterisation provides benefits when managing the large databases of LBL transmittances and when adding new variable gases (these are issues in the visible/IR, but do not affect the microwave). For the old parameterisation, LBL simulations are required for the total transmittances including all atmospheric constituents, and then further simulations each time omitting an additional variable gas. In this way a transmittance database is constructed which is used to calculate the effective transmittances illustrated in Eq.
(  1 ). A separate transmittance database is required for each configuration of variable gases. When adding a new variable gas it may be necessary to run the LBL model multiple times as a number of different sets of transmittances may need to be updated within a transmittance database. Furthermore, when developing predictors for the new variable gas, one may have to consider predictors involving other trace gases in particular spectral regions due to the fact that the effective transmittances are not dependent on one single gas alone. By contrast, only one transmittance database is required for the new scheme: LBL simulations are required for the transmittances due to each variable gas alone, the transmittances for the mixed gases excluding each combination of variable gases to be supported in the RTTOV simulations, and the total transmittances with each combination of variable gases varying among the training profiles.
Comparisons to the LBL model for independent profiles It is also important to validate RTTOV against the LBL models for profile sets that are independent of the training profiles. This section presents comparisons of RTTOV IR radiances with radiances computed from the LBLRTM model run at a spectral 245 resolution of 0. 001 cm -1. The surface emissivity is again set to one, and the LBLRTM radiances are integrated over the channel spectral response functions and compared to the RTTOV radiances. These are therefore comparisons of RTTOV to the ideal scenario of running a LBL model instead. Figure  6  shows the differences between RTTOV and LBLRTM for the MSG-4 SEVIRI IR channels for the 5000 profiles in the temperature subset of the NWP SAF 25000 diverse profile dataset on 137 levels  (Eresmaa and McNally, 2014)   Figure  7  shows statistics for IASI for the same 5000 profiles over the first 6 secants used in coefficient training for the v7 predictors on 101 levels (variable O 3 ) and the v13 predictors on 101 levels with variable O 3 and CO 2.
Finally, Figure  9  shows a comparison for the ATMS microwave sensor. This plot shows statistics comparing RTTOV with radiances computed from channel-integrated LBL transmittances for v7 and v13 predictors for all 25000 profiles in the NWP SAF diverse profile dataset on 137 levels over the 6 secants used in training. For microwave sensors, radiances computed from channel-integrated transmittances are very similar to channel-integrated radiances from high resolution transmittances, so only the former are shown here. The differences between RTTOV and the LBL are much smaller in the microwave than in the IR because the errors due to summing polychromatic optical depths are smaller since the absorption spectrum is much more smooth in the microwave than the IR. For the temperature sounding and window channels (1-16) there is no significant difference between the old and new predictors.
In particular there is a notable reduction in standard deviation of up to 0. 2 K with the new predictors for IASI in the ozone band around 1050 cm -1. This result will be further evaluated at other NWP centres. The new predictors are expected to be more computationally expensive than the old parameterisation due to the additional cost of the correction term calculation. Testing indicates that the new predictors are up to 30% slower for the direct and tangent on the compiler and the type of simulation (for example, the number of variable gases). In the context of an operational data assimilation system this increase should not present significant problems. Future work will involve validation of the accuracy of the new predictors in operational applications such as NWP assimilation systems, as well as off-line studies comparing RTTOV with other radiative transfer models such as CRTM.
Simulations 250 were run for all 14 secants used in training the coefficients for the v9 and v13 predictors with variable O 3 and CO 2. The profile dataset includes variable water vapour and O 3 , and all other gases use fixed background profiles. While there is an increase in bias with the v13 predictors in the water vapour and CO 2 channels (6. 3, 7. 3 and 13. 4 µm), the new predictors result in.