dataesr/scientific-paragraphs-categorization huggingface dataset:
" Investissements d'avenir" program. This support, managed by the National Research Agency (French ANR), is referred to as ANR-10-EQPX-40. Mediterranean activity was partially cofunded by the remOcean (European Research Council, grant 246777) and BioArgo (CNES-TOSCA) projects. The Canadian Foundation for Innovations (CFI) John R. Evans Leaders Fund #30124 covered the acquisition of 9 Pro-Ice floats. The Canadian Excellence Research Chair in Remote Sensing of Canada's New Arctic Frontier covered the wages of some research assistants. The European Commission Horizon 2020 grant #727890 to the Integrated Arctic Observation System INTAROS covered travel expenses related to the deployment of the Pro-Ice floats and coordination efforts. Regarding Pro-Ice deployments, support was given by NSERC for ship-time: • 2016: NSERC Ship-Time program (# RGPST-486341-2016).
The experimental RPC-MIP responses also exhibited narrow peaks with smaller amplitude that are associated with interferences with the platform or other instruments. They were mostly present at 49 kHz harmonics, at 266 kHz, and 800 kHz. Such instrumental artifacts have been taken into account in this analysis by removing their signature as much as possible. Detailed information on these interferences and on the noise level of RPC-MIP mutual impedance responses is given in the RPC-MIP user guide  (Henri et al. (2019) ), which is available in the Planetary Science Archive RPC-MIP archive  (Henri et al. (2018) ). A database of 1350 simulated RPC-MIP responses was generated with µ ∈ [0. 1 ; 0. 9] and τ ∈ [10 ; 100]. We note that µ = 0 or 1 corresponds to a single Maxwellian eedf that never provided simulation responses in accordance with experimental responses in which a clear plasma signature emerged from the noise.
Nearly 65% of these selected responses have been found in accordance with one of the simulated responses taken from the database, with an average absolute dierence below 1. 5 dB (see g. 1, which displays two experimental responses taken on 24 August 2016 that correspond to similar plasma parameters). The stacked bar charts in g. 2 sort the daily responses into three categories. The rst category contains responses in which no plasma signature emerged from the noise (1569509 responses from January to September 2016). The second category contains responses in which a clear plasma signature emerged from the noise, but that were not well t by the simulations (305782 responses), and the last category gathers the responses in which a clear plasma signature emerged from the noise that were well t by a simulated response in the database (569441 responses).
A t of the simulated response to the experimental data was considered good when the average absolute dierence between them was lower than 1. 5 dB. Above this value, the tting was considered poor and was therefore discarded. Previous simulations have found that the response of the probe is particularly sensitive to the electron temperature and density ratio in the vicinity of the plasma frequency, and to a lesser extent, to the sheath thickness, which is strongly related to the limit of the response level of the probe above the plasma frequency  (Wattieaux, G. et al. 2019). It is therefore expected to observe experimental responses with a clear plasma signature that have not been computed during the build-up of the simulation database. Increasing the resolution on the temperature and density ratio in the simulations and computing the response of the probe for dierent sheath thickness appeared useless because the number of good ttings was high enough to follow the plasma evolution around 67P from January to September 2016.
Conclusion The cometary plasma around comet 67P has been characterized based on the response of the mutual impedance experiment of the Rosetta mission RPC-MIP. We focused on the last nine months of the mission, when the location of the Rosetta orbiter around comet 67P and the experimental conditions for the mutual impedance probe were most appropriate for extracting plasma parameters in the inner coma of comet 67P. It was possible to characterize the plasma after a compliant electrostatic modeling of the probe had been achieved, which allowed us to set up a simulated responses database. This database was then compared with the experimental responses of the probe. As a result, the mutual impedance probe responses provided by the RPC-MIP instrument that presented a high enough signal-to-noise ratio were found to be consistent with the presence, in the ionized environment of comet 67P, of two well-dened electron populations.
Acknowledgements. The Rosetta RPC-MIP data is available at  ESA's Planetary Science Archive (PSA). Work at LPC2E/CNRS was supported by  CNES  and by  ANR  under the nancial agreement  ANR-15-CE31-0009-01. We acknowledge  PRACE  for awarding us access to  Joliot-Curie  at  GENCI@CEA, France. This work was supported in part by  NASA Solar System Exploration Research Virtual Institute (SSERVI) :  Institute for Modeling Plasmas, Atmosphere, and Cosmic Dust (IMPACT) , and the  NASA High-End Computing (HEC) Program  through the  NASA Advanced Supercomputing (NAS) Division at Ames Research Center. J. D. acknowledges support from  NASA 's  Rosetta Data Analysis Program , Grant No. 80NSSC19K1305.
As lawsonite blueschist is much more common than lawsonite eclogite, we survey the composition of lawsonite in representative localities of blueschist, including blueschist associated with eclogite (lawsonite-bearing, epidote-bearing), and blueschist not associated with eclogite at current exposure levels. Included in this review are metabasaltic rocks, silica-and carbonate-rich metasedimentary rocks, metasomatic rocks, and lawsonite-rich veins. This dataset demonstrates that lawsonite composition is a sensitive indicator of reaction history during subduction and exhumation, and specifically of fluid-rock interaction, with implications for element cycling in subduction zones. Furthermore, most exhumed lawsonite eclogite records slab-surface conditions that correspond to the location where the slab-mantle interface transitions from decoupled to coupled, and therefore provides key insights into the thermal history and dynamics of subduction zones.
Lawsonite geochronology A compilation of ages of lawsonite blueschist and eclogite shows that, with the exception of the Neoproterozoic blueschist of Anglesey (Wales, UK), all others are Phanerozoic (Fig. 17 ). This may in part be a function of the lack of preservation, as Proterozoic HP/LT rocks in general are rare. However, given the significance of lawsonite for indicating cooler subduction geotherms, it is important to evaluate possible trends in formation of lawsonite-bearing rocks. For example,  Tsujimori and Ernst (2014)  noted that there is no difference in the occurrence of lawsonite vs. epidote blueschists in Phanerozoic subduction complexes, and therefore no overall global change in subduction geotherms in that time. An apparent "hiatus" in lawsonite formation in the Permian was attributed to processes related to the breakup of Pangaea, although subsequent work has determined Permian ages for lawsonite blueschist and eclogite (Fig.
17 ). Most studies of the metamorphic age of lawsonite-bearing rocks have employed methods that did not directly involve analyzing lawsonite. The Lu-Hf isotopic system can be used to determine the age of HP/LT metamorphism through analysis of lawsonite, other HP/LT minerals (garnet, omphacite), and whole-rock composition, but this method has thus far only been applied in a few studies  (Mulcahy et al. , 2011; Vitale Brovarone and Herwartz, 2013; Mulcahy et al. , 2014; Tamblyn et al. , 2019). Analyses of Franciscan lawsonite blueschists from two regions of the complex yielded ages of c. 145 Ma (Ring Mountain) and c. 152 (Ward Creek), within the range of 157-141 Ma determined by other methods from HP rocks in the Franciscan Fig. 11. Lawsonite Al-site composition, with data from a wide range of localities and rock types (complete dataset with references in Table  S1 ).
We    12. Pb vs. Sr in lawsonite from six localities including data from eclogite and blueschist-facies mafic rocks (metabasalt, metagabbro), metasediment (quartzite), and metasomatic rocks (lawsonite-chlorite rocks, lawsonite-rich veins and layers) (data and data sources are in Table  9 ). consider the role of lawsonite in the subduction component of the deep-Earth water cycle and discuss how the geochemistry of lawsonite provides new information about fluid-rock reaction during HP/LT metamorphism. We then use the compositional dataset summarized in this paper to discuss how lawsonite composition and zoning may be used to document subduction conditions and processes.
, 1989). Results of a survey of ~300 HP/LT metabasaltic rocks from 30 subduction complexes (Fig. 18 , Table  S3 ) indicates that epidote blueschist and eclogite have volatile contents (inferred primarily from LOI and assumed to be dominantly H 2 O) of ~2. 0-2. 7%, with most ranging between 0. 5 and 5 wt% and with more eclogites than blueschist with <1 wt% H 2 O. Lawsonite-bearing metabasites typically have higher H 2 O content: most lawsonite blueschists range from ~2. 5 to 8. 5 wt% (a few are even more H 2 O-rich), with an average of ~5%, and lawsonite eclogites range from ~1. 2 to 6. 4 wt% (average ~3%) (Fig. 18 , Table  S3 ). According to this dataset, most lawsonite-bearing metabasalts have more H 2 O than predicted by phase equilibrium modeling. Lawsonite-bearing metagabbro and metatroctolite (e.
, 2015). Indeed, even with a revised P-T dataset that includes more lawsonite eclogites (e. g. , Port Macquarie, Australia; central Pontides, Turkey) and that excludes rocks that experienced ultrahigh-pressure conditions of continental subduction (Table  S4 ), results show that most blueschist-facies rocks record higher-temperature conditions than modeled slab-surface temperatures for a typical (normal/cold) subduction zone (e. g. ,  Syracuse et al. , 2010) (Fig. 19 ). However, it is interesting that a majority of HP/LT rocks plot in the lawsonite stability field (>3x as many as in the epidote stability field, though results vary depending on which lawsonite-out reaction is assumed). In addition, most lawsonite eclogitesand all but one with fresh matrix lawsonite plot in the realm of modeled slab conditions  (Figs.
g. , Mn from garnet; Na or K from white mica). (2) obtain microstructural data for lawsonite, including systematic study of the crystallographic preferred orientation (CPO) of lawsonite in different rock types recording eclogite-and blueschistfacies conditions, for better understanding of the rheology and geophysical properties and dynamics of subducted oceanic crust at forearc to subarc depths. Electron back-scatter diffraction (EBSD) analysis of lawsonite CPO should be accompanied by information about the overall mineral assemblage (including mode) and an evaluation of the fabric data in the context of textural generation of lawsonite and P-T path. Comprehensive, high-quality datasets that integrate composition with microstructure allow evaluation of the parameters that control trace-element incorporation in lawsonite (crystallographic effects, P-T-X conditionsincluding fO 2 ).
Acknowledgements We thank the following colleagues for sharing lawsonite-bearing samples:  Carly Faber  (Yukon),  George Harlow  (Guatemala),  Sean Mulcahy  (Franciscan),  Tatsuki Tsujimori  (Franciscan, Guatemala), and  Richard Palin  and  David Hernandez-Uribe  (Garnet Ridge). Constructive reviews by  Zeb Page  and  Ethan Baxter  helped us improve the manuscript, and we also appreciate the editorial advice of  Marco Scambelluri. This research has been supported by the  University of Minnesota   Grantin-Aid program  and  NSF  grant  EAR-1949895  to  D. L. Whitney.
The Seine River Axis During the Past Four Decades The existence of numerous electrofishing data since the early 1980s makes it possible to address in some detail the changes in fish communities along the Seine River axis over the past four decades, a pivotal period for a better understanding of the ongoing recovery processes. For this purpose, we focused our analysis on the Seine River course, from its confluence with the Aube River, about 200 km upstream from Paris, to the estuary. Variations of the French fish index (IPR) values and its metrics over time were examined. The IPR (Indice Poissons Rivière, i. e. Fish River Index)  [32]  was developed to measure the state of river fish communities. It is composed of seven metrics, based on the number of species or fish abundance, reflecting the community's functional or structural features.
Data from one healthy control participant had to be excluded due to poor EEG data quality. The final sample size consisted of 40 participants, of whom 20 were patients with AN (mean age = 22. 7 years, SD = 6. 51, age range 18 to 43, mean BMI = 16. 6 kg/m 2 , SD = 1. 1), and 20 were HC (mean age = 21. 0 years, SD = 5. 12, age range 18 to 39; mean BMI = 20. 7 kg/m 2 , SD = 1. 78). Among AN group, 9 patients (45%) reported psychotropic treatment (antidepressants: n = 4, 20%; anxiolytics: n = 1 5%; both: n = 4, 20%). Exclusion criterion for all participants were: (a) being male, (b) younger than 18 years, (c) current or life-time history of chronic illness or neurological condition (abnormal EEG activity), which could influence electrophysiology and/or the neuropsychological assessment, (c) lifetime diagnosis of a severe mental health condition (bipolar disorder, lifetime diagnosis of psychotic disorder), (d) current substance dependence or any other mental disorder that could interfere cortical activity or the assessment.
Afterwards, they completed a battery of self-reported questionnaires (DERS, ERQ, SCL-90-R, YFAS-2). Next, participants performed the experimental tasks (food craving and emotion regulation) during EEG acquisition. Participants were instructed to have a 'normal' meal 90 minutes before the session and then to refrain from eating or drinking coffee. Additional information was collected on the day of the experimental session, in order to control for a set of variables (i. e. , food consumed on the day of the session, menstrual cycle, and alcohol or drugs consumption in the last 24h). In a second session, participants completed a different set of experimental neurophysiological tests (data will be reported in separate manuscript).
Statistical Analysis Statistical analysis was carried out with Stata Statistical Software: Release 15 for Windows  [67]. The variables of the study (ERQ, YFAS, DERS and SCL-90-R) were compared between groups using t-tests for quantitative measures and chi square (χ 2 ) tests for categorical measures. Comparisons were considered significant with p < 0. 05 after Bonferroni-Finner correction to avoid Type-I errors  (Finner, 1993). The effect size for the mean differences/proportions was measured through Cohen's-d coefficient (low/small effect size was considered for |d| > 0. 2, moderate for |d| > 0. 5 and large/high for |d| > 0. 8; Kelly and Preacher, 2012). In this study, different dimensional and categorical measures for the YFAS 2. 0 were analysed: firstly, the YFAS 2. 0 dimensional symptom count, which measures the 11 DSM-5 SRAD criteria (raw scores are in the range of 0-11); and secondly, the categorical classification based on the dimensional symptom count, a threshold for food addiction (presents for individuals with at least two symptoms plus self-reported clinically significant impairment or distress, and absent for participants who did not meet these criteria).
Funding: This manuscript and research were supported by grants from the  Instituto de Salud Carlos III (ISCIII)  [ FIS PI14/00290 ,  PI17/01167  and cofounded by  FEDER funds/European Regional Development Fund (ERDF) , a way to build Europe]. CIBER Fisiopatología de la Obesidad y Nutrición (CIBERobn)  is an initiative of ISCIII. M. L-M and C. V-A are supported by a predoctoral grants of the  Ministerio de Educación, Cultura y Deporte  ( FPU15/02911  and  FPU16/01453 ). Study resulting from the  SLT006/17/00246  grant, funded by the  Department of Health of the Generalitat de Catalunya  by the call " Acció instrumental de programes de recerca orientats en l'àmbit de la recerca i la innovació en salut ". We thank  CERCA Programme / Generalitat de Catalunya  for Institutional support.
Ocean and sea ice initial conditions are derived similarly to CNRM-CM6-1 from a run constrained towards Glorys 2V4, except for sea ice concentration which evolves freely in the NEMO-Gelato run to initialize MF-Sys6. Beyond the coupled GCMs used, the re-forecasts compared in this study use different initialization strategies and ensemble generation techniques. This can impact the ensemble spread and forecast quality. The study evaluates monthly mean sea ice extent (SIE) derived from sea ice concentration (SIC), using the 15% SIC threshold to define presence or absence of sea ice. These fields are compared to reference data provided by the National Snow and Ice Data Center (NSIDC) version 4, based on brightness temperature  (Cavalieri et al. 1996). Throughout the re-forecast period, NSIDC SIC data is missing in some areas north of 85°N.
This simple method has some caveats, since for bounded fields such as SIC values it can yield values outside the theoretical range. We simply correct "out of bounds" values by setting negative SIC values to 0 and SIC values higher than 100% to 100%. The second method uses also a leave-one-out cross-validation, but to trend-adjust (TA) the data: we adjust the SIC of each model (either ensemble mean or member) at a given grid-point as well as NSIDC data for a linear trend. In this study we chose to remove the linear trend and then compute anomalies with respect to the 1993-2014 mean. The obtained SIC values are then adjusted to the [0,1] range before computation of the indices described in the following section. Note that more elaborate trend-adjustment techniques for SIC have been introduced in past works such as  Dirkson et al.
However, SEAS5 SIE values are comparable to NSIDC in the beginning of the re-forecast period but are then overestimated with respect to NSIDC after 2006, due to a too weak negative trend in the re-forecast (about one third of the linear trend estimated in NSIDC). The four other models also underestimate the amplitude of the negative trend, but much less so, with values ranging from -83,000 to -96,000 squared kilometers per year of loss in Pan-Arctic SIE. This underestimation of the negative trend in SIE was also found, although over a different re-forecast period and using a different model, by  Wang et al. (2013). In the case of GloSea5 and EC-Earth 3. 2, SIE computed from NSIDC data are inside the range of the ensemble for almost all years of the re-forecast period. The spread of SEAS5 appears to be slightly lower than the other two operational seasonal re-forecast systems, GloSea5 and MF-Sys6.
Correlation drops (as expected) with lead time, from above 0. 8 in May to near-zero correlation for two of the models in October, namely CNRM-CM6-1 and EC-Earth 3. 2, although in the case of the latter, this may be due to the smaller ensemble size. The three operational systems generally exhibit significant levels of correlation with NSIDC data at a 6-month lead time, although MF-Sys6 drops below the 95% significance threshold for August and September SIE (see Table  2  for September RMSE and correlation values). As expected from the results of the individual models, the C3S-MME (in orange) outperforms the MME for both metrics in the long forecast times. All models show higher skill than persistence, although the score for persistence is included inside the range of uncertainty of the scores (based on a χ² for RMS and a Fisher test for correlation) after 2 months forecast time in most cases (not shown).
However, in the case of the 2012 minimum, past studies using observational data and GCM experiments suggest that the role of an extreme summer storm over the Arctic was minor compared to sea ice preconditioning and warmer near-surface atmospheric temperature conditions during the summer season  (Zhang et al. 2013 , Guemas et al. 2013). The operational systems show generally less variability in the misplacement error than CNRM-CM6-1 and EC-Earth 3. 2, apart from MF-Sys6 in the first half of the re-forecast period. This suggests that for the former, skill evaluations based on RMSE of Pan-Arctic SIE are giving a rather accurate picture of the model capacity to predict the sea ice edge position, whereas for the latter two systems, the Pan-Arctic SIE may "hide" some compensation between areas where SIC is overestimated and where it is underestimated.
For some systems, IIEE tends to grow during the re-forecast period, which may be related to the strong decrease in total SIE during 1993-2014. We therefore re-compute the IIEE score after trendadjusting the SIC as described in section 2. 2. Results are shown in Fig. 5. With this SIC trend adjustment, the minimum over the period is 2007 (and 2012 no longer appears as a year with low SIE). All models miss the 2007 anomaly with a large AEE. As found previously using SIC data corrected for the mean bias, the AEE is the largest contribution (on average) for September IIEE in all systems. In order to evaluate the impact of trend-adjustment on IIEE, and also extend the analysis to the other months of the re-forecasts, we show in Fig. 6  the mean evolution as a function of forecast time of the IIEE in the different models and both MMEs considered, using bias-corrected SIC data (left) and trend-adjusted SIC data (right).
Probabilistic re-forecasts of sea ice edge We compute the SPS using monthly SIC re-forecasts and a 15% SIC threshold for presence or absence of sea ice, with two approaches to bias-adjust the model data over the re-forecast period. The first method used is a grid-point bias correction of the probabilities for each model (or multi-model) to exceed the 0. 15 threshold, using leave-one-out cross-validation. Probabilities exceeding 1 or below 0 are readjusted to 1 or 0, respectively. The second method uses the same adjustment of probabilities, but computes these after applying the trend adjustment to the SIC values. Figure  7  shows results according to the forecast month for each individual model as well as the 5-model and C3S MMEs. Consistent with deterministic results for the IIEE, the MMEs rank among the best models (low SPS) for each forecast time with the first adjustment, and tend to outperform all the individual systems after the second adjustment, but skill scores are not significantly better.
For the Beaufort-Chukchi and Laptev-East Siberian seas, all models exhibit similar evolutions with forecast time, quite similar to the total SIE over the region. As for the total Pan-Arctic region, IIEE is maximum when the SIE is minimum, and then drops in October. May IIEE is close to zero for each system, suggesting that models are correctly initialized as fully ice-covered over these regions and error grows quite slowly initially. In the Laptev-East Siberian seas sector, the October IIEE is very similar for all systems due to the annual cycle of sea ice extent over the region: only a few years in NSIDC data show some ice-free areas in these seas, and they are generally not captured by the different forecasting systems at such long forecast ranges (not shown). The MME and C3S MME IIEE values nearly overlap (black and orange lines), indicating that the CNRM-CM6-1 and EC-Earth 3 models do not necessarily provide additional value to the multi-model approach in these areas.