dataesr/scientific-paragraphs-categorization huggingface dataset:
pangaea. de; ncei. noaa. gov) were searched. In case datasets were unavailable from repositories, the lead authors were contacted directly. In case of published data unavailable from repositories or authors, plots of the original publications were digitized, if possible  (Rietti-Shati et al. , 1998;  https://doi. org/10. 5194/cp-2022-96 Preprint. Discussion started: 24 January 2023 c Author(s) 2023. CC BY 4. 0 License. Hu and Shemesh, 2003). Where digitizing plots was not feasible, records were excluded from further consideration; this was the case for  Chondrogianni et al. (2004)  and  Hu et al. (2003). Chronologies were adapted from the original publications, where available and stored in cal yrs BP format (relative to 1950 CE). Sample ages given in different formats (e. g. b2K or CE) were converted to cal yrs BP.
This procedure also applies to radiocarbon-based chronologies. Chronologies were not recalculated because the effect of different 14 C-calibrations and different age model approaches is supposedly minor with regard to the aim of this study. This is especially true when considering the fact that high-resolution datasets (annual to decadal resolution) are the exception among all datasets identified. The error introduced by the different age models is, thus, considered minor when compared to the resolution of the datasets. For applications requiring more precise chronologies and better comparability of datasets, we provide the radiocarbon measurements of respective datasets as well. This enables future users to create tailor-made chronologies if needed. Datasets without chronologies were stored using depth notation only.
For further analysis, datasets were partly regrouped and combined to generate longer continuous δ 18 OBSi datasets, henceforth referred to as «records». This comprised treating datasets with data from several coring sites or outcrops from the same lake as one single record, in agreement with the author's original interpretation  (Quesada et al. , 2015; Swann et al. , 2018). Data stemming from the same sediment core but published in different manuscripts were combined into single continuous records. This applies to records from Lake Kotokel  (Kostrova et al. , 2013a; Kostrova et al. , 2013b; Kostrova et al. , 2014; Kostrova et al. , 2016)  and Lago Chungará  (Hernandez et al. , 2008; Hernandez et al. , 2010; Hernandez et al. , 2011; Hernandez et al. , 2013). Likewise, data from different cores presented in different publications but stemming from identical or reasonably similar coring sites were combined to single records.
Published datasets Following a thorough literature survey, a total number of 71 published down-core datasets of δ 18 OBSi from 64 sites has been identified. Ever since the first records of δ 18 OBSi from lake sediments have been published  (Rietti-Shati et al. , 1998; Shemesh and Peteet, 1998) , there has been a growing research interest, manifest in an increasing number of publications (Fig. A1 ). Only recently has there been a stagnation of the number of records published. A detailed overview of the identified publications and the records presented therein is provided in Tab. A2. Most publications give the δ 18 OBSi data as time series, as is usual in paleoenvironmental studies. The chronologies rely on a wide variety of dating methods (Fig. A2 ), with 14 C (used on 46 records) being by far the most frequent.
org/10. 5194/cp-2022-96 Preprint. Discussion started: 24 January 2023 c Author(s) 2023. CC BY 4. 0 License. therefore not included in the evaluation of the dataset. Catchment sizes vary by several orders of magnitude as well. For small lakes, catchments are often <10 km² (N=13, e. g. #22, 33, 37), whereas the largest lakes, Lake Malawi and Lake Baikal feature catchments of 128,727 km² and 569,176 km², respectively (Fig. 4A ). Therefore, these lakes integrate the environmental signal over a large and potentially diverse region in terms of hydrology. While most of the lakes compiled in this study are primarily fed by surface runoff and/or precipitation (according to the original publications of records), groundwater influx may also play a pivotal role. Groundwater influx may introduce a large memory effect of past precipitation due to the generally long residence times of aquifers.
However, some records do not follow this general pattern after mean removal (Fig. 6C ). This could be indicative of the fact that the dataset still features lakes with different hydrological settings and tres which have a substantial impact on the recorded signal. Filtering for hydrologically open lakes (Fig. 6D, N=13 ) and tres<100 yrs (Fig. 6E , N=10) displays a clear decreasing δ 18 OBSi trend of 2. 5‰ throughout the Holocene. Differences between individual records do exist, though. Most notably, the absolute δ 18 OBSi maxima of individual records do not occur at the same time. While some records feature maxima at the beginning of the Holocene, other records feature maxima between 5 and 8 kyr BP. This pattern is in line with Holocene Tair reconstructions which have found spatial differences of the timing of the Holocene thermal maximum  (Kaufman et al.
Since datapoints of 1850 CE and younger have been removed from the dataset prior to analysis to exclude the industrial era, a potential effect of recent climate change is not covered in this subset of data. As with the Holocene maximum, sites in Alaska also differ with regard to Holocene minimum. They tendentially show absolute minima earlier than Eurasian records (3 and 4 kyr BP, respectively). This underlines once more, the different behaviour of the regions (North America and Eurasia).
Changes of annual Tair and July Tair relative to modern values were retrieved from the pollen dataset. Annual and July Tair show similar patterns with a pronounced early Holocene increase, followed by middle Holocene maximum and a less pronounced decrease until the present day. Both the middle Holocene maximum and the subsequent decrease of Tair are in good agreement with the combined Eurasian δ 18 OBSi trend (Fig. 8A, 8D ). This further supports a substantial influence of Tair on the δ 18 OBSi signal. However, the early Holocene maximum of δ 18 OBSi in our combined NH and Eurasian trends is not reflected by pollen-based reconstructions. Caution has to be applied when using pollen-based climate reconstructions for comparison because vegetation changes may lag behind changes of climatic variables  (Herzschuh et al.
Frontiers and Challenges (Records older than MIS 1) Further back in time than MIS 1, coverage with δ 18 OBSi records is generally limited. As MIS 1 (N=37) and MIS 2 (N=18) have the highest numbers of records, they offer the best possibility of comparing glacial and interglacial records. Records beyond MIS 2 are very sparse and do not allow for filtering and generating common trends. Here, the interpretation has to rely on comparison of individual datasets. There are few (N=16) records only which cross the MIS 1-MIS 2 boundary and these records cover neither MIS 1 nor MIS 2 entirely. A quantitative comparison of MIS 1 and MIS 2 is therefore difficult. Again, caution has to be applied due to the fact that these records stem from lakes with different hydrological and climatic settings (i. e. maritime Alaskan sites vs.
continental Siberian sites). The most promising approach for investigating MIS 1 and MIS 2 is extending the NH MIS 1 datasets to the past (Fig. 9 ). Five NH δ 18 OBSi records  (#3,8,15,24,25)  extend back from MIS 1 into MIS 2 and offer insight into the hydroclimate history of Siberia during the last glacial and the deglaciation. Most of these records display a maximum near the MIS 1-MIS 2 boundary and a decrease of δ 18 OBSi with increasing age in MIS 2, suggesting decreasing Tair. This is the case for all records except #3 which displays a relative δ 18 OBSi maximum at ca. 16 kyr BP. This maximum may also be caused by dry conditions outperforming the effect of lower Tair. Only records #3, #8 and #15 extend beyond 13 kyr BP. Both #3 and #15 reach their absolute minima at 23 kyr BP and 24 kyr BP, respectively.
Acknowledgements PM is funded by  German Ministry of Education and Research  via the  Palmod2-Projek  ( 01LP1923B ). HB is funded by the  Academy of Finland  (grant  348536 ). AH is funded by the  Spanish Ministry of Science and Innovation  through the  Ramón y Cajal Scheme  ( RYC2020-029253-I ). We thank xx (anonymous) reviewers whose comments helped improving the manuscript. We also thank all scientists and technicians developing and advancing the research field of oxygen isotopes in diatom silica. https://doi. org/10. 5194/cp-2022-96 Preprint. Discussion started: 24 January 2023 c Author(s) 2023. CC BY 4. 0 License.
2006 ) and through LiDAR (Light Detection And Ranging) observations from multiple locations  (Jäger 2005). In addition, satellite observations from the Stratospheric Aerosol and Gas Experiment (SAGE) missions provide one of the longest records of stratospheric aerosol optical properties since the mid-70s and were used to develop long-term databases such as the Global Space-based Stratospheric Aerosol Climatology  (Kovilakam et al. 2020). Figure  1  shows how the stratospheric aerosol layer in the Northern Hemisphere has been affected by major and moderate volcanic events since the late 70's. Over the last decade, major wildfires affected the stratosphere at levels never observed before. The 2017 British Columbia wildfires and 2019/20 Australian bushfires affected the stratosphere for several months resulting in Stratospheric Aerosol Optical Depth comparable to medium volcanic eruptions  (Torres et al.
Vertical profiles show an increase in CO and CH 4 concentrations between 6. 5 and 11 km, as well as two distinct levels of CO 2 concentrations at the same altitudes (shown in cyan and magenta on Figure  3 ). This difference in concentration suggests the presence of air masses of different origin. Back-trajectories from the two median levels (7. 5km and 10km) were calculated using HYSPLIT trajectory software  (Stein et al. 2015)  and show that one of the air masses comes from a low-lying area different from the first (Figure  3b ). Using the Ozone Mapping and Profiler Suite-National Polar orbiting (OMPS)/Partnership (NPP) Aerosol Index swath orbital V2 (Torres 2019) associated with air mass back-trajectories, it appears that the two trajectories encountered different plumes on September 4th and on September 5th as shown in Figure  3c  and 3d (thicker lines correspond to the position of air masses on a given day).
Campaign Results The main goals of the REAS campaign were to: 1) characterize stratospheric aerosol properties using multiple balloon-borne instruments, 2) test in-flight new instruments like the IMPACTOR that cannot normally fly in Europe due to their weight and 3) conduct instrument inter-comparison thanks to the medium flight possibilities. The deployment of medium flights was used to gather data from multiple instruments. This allowed us to characterize the chemical composition of aerosols from the surface to the balloon burst altitude, revealing the presence of organic material in the upper troposphere and lower stratosphere (UTLS), alongside with the aerosol microphysical properties among different vertical layers  (Benoit et al. 2023). The balloon measurements conducted during the REAS campaign were predominantly affected by the long-range transport of the La Soufrière eruption.
Acknowledgments. Jean-Paul Vernier  thanks the  STUDIUM Smart Loire Valley Programme  for granting a visiting researcher award at the  LPC2E laboratory. The authors gratefully acknowledge funding from the  Labex Voltaire  ( ANR-10-LABX-100-01 ), from  CEFIPRA  (project  n°6409-1 ), from the  ASTuS ANR  project ( 21-CE01-0007-01 ), support from the  NASA   Roses Upper Atmospheric Composition Observation program  for the balloon flights, support through the  HEMERA  infrastructure via the  European Union 's  Horizon 2020 research and Innovation program  (grant  N°730970 ). We thank  M. Fromm  for his feedbacks on the description of the pyroCb events which affected the stratosphere during the summer/fall 2021. We thank  Johnny Mau  and  Amit Pandit  for their technical supports in the payload preparations.
Numerical simulation of sample pooling The numerical simulation of sample pooling methods was based on the Dorfman pooling strategy  (Dorfman, 1943)  and on the choice of four variables taken to best describe the context of the study: (i) the distribution of nDL-PCB concentrations in the pork meat, (ii) the prevalence of a contamination greater than the maximum limit value (ML = 40 ppb in this case study), (iii) the uncertainty in the nDL-PCB measurement, and (iv) the size n of the sample pools. The distribution of PCB concentrations in pork meat was determined from a database of 96 samples collected as part of the Safety of Organic Meat Research Project (Dervilly-Pinel et al. , 2017) and of 589 samples from the 2019 report of the French General Directorate for Food on control and monitoring plans (DGAL, 2019).
The PCB concentrations in these databases closely fitted a log-normal distribution, consistent with the literature  (Jönsson et al. , 2004; Leith et al. , 2010). For the simulations of non-contaminated samples, we used a log-normal distribution characterized by a mean (in log-scaled units) of -0. 38 and a standard deviation (in log-scaled units) of 0. 85, which corresponds to an expected average value for [Σ6nDL-PCBs] of 0. 98 (standard deviation = 1. 01). For the simulation of contaminated samples, values were taken from the right tail of a log-normal distribution shifted to a mean, in log-scale, of 0. 30 (i. e. , an expected average value for [Σ6nDL-PCBs] of 1. 94). The prevalence p of contamination (proportion of samples whose Σ6nDL-PCB concentration exceeded the ML) was a further simulation variable.
Linearity of PCB measurement in pools of meat samples In order to assess how the trueness of the PCB measurement is influenced in a pool of n samples, the linearity of the dilution process due to the pooling (i. e. , mixing a contaminated sample with n noncontaminated samples) is displayed on Fig. 3. The concentrations of the six nDL-PCBs were determined in different pools of 10 to 200 samples, all containing one sample spiked at a constant concentration (82 ng/g fw). On examining the dilution effect related to pooling, a linear relationship between PCB measurement and pool size was found for the six nDL-PCBs, all the coefficients of determination for all the tested pools exceeding 0. 99. The coefficient of determination of the sum of the six nDL-PCBs also exceeded 0. 99 for all the pools tested (data not shown).
Acknowledgments This "proof of concept" research was funded by the  French National Research Agency  (contract No. ANR-19-CEI21-0011 ; " SENTINEL: Highthroughput screening tools for a reinforced chemical safety surveillance of food "), and is expanded in the framework of the  SAFFI Horizon 2020 research and innovation action  funded by the  European Union  (grant agreement No. 861917 ; "SAFFI:  Safe Food  for Infants in China and the  EU"). The authors thank  Camille Lebris  (fixed-term contract in INRAE), for her involvement in the practical part of pooling implementation,  Stephane Andanson , the engineer in charge of analytical method validation for  INRAE  ( UMR PIAF, Clermont-Ferrand ), for advice on data accuracy and uncertainty, and  Aurélie Promeyrat  ( IFIP, Paris ) for her involvement in interaction with the  DGAL (French general directorate for food, Paris)  for the use of the data of monitoring and control plans of chemical contaminants in French meat.
Open Research Section The Iber+ software used to perfom the computation showed in this paper, as well as the four test cases and the related data are all openly and permanently available at https://entrepot. recherche. data. gouv. fr/dataverse/soilsedimentmodellingdata. and C. L. were supported by the Labex OSUG@2020 (Investissements d'Avenir, grant agreement ANR-10-LABX-0056). G. P. was supported by the LabEx Tec21 (Investissements d'avenir -grant agreement n°ANR-11-LABX-0030). Some of the computations presented in this paper were performed using the GRICAD high performance infrastructure (https://gricad. univgrenoble-alpes. fr), which is supported by the Rhône-Alpes region (GRANT CPER07_13 CIRA) and the Equip@Meso project (Investissements d'avenir -grant agreement n°ANR-10-EQPX-29-01).
manuscript submitted to Water Resources Research Acknowledgments O. G. -F. was funded by the  Spanish Ministerio de Universidades  under application  33. 50. 460A. 752 , by the  European Union NextGenerationEU/PRTR  through a contract  Margarita Salas  from the  University of Vigo , by the  IGE lab  and by the  French Ministry of Environment (Direction Générale de la Prévention des Risques Ministère de la Transition Ecologique et Solidaire)  within the multirisk agreement  SRNH-IRSTEA   2020  ( Action TorRex ). G. N. work. This way of building the mesh has the advantage of using a higher spatial resolution in the regions where the water and sediment fluxes concentrate, i. e. in the river streams and in the badlands. Similar discretisation schemes for solving the 2D-SWE in hydrological applications have been used for instance in Cea et al.
(2022);  Costabile  and Costanzo (2021); Ferraro et al. (2020). The main river network was defined from a DTM of the catchment with a spatial resolution of 1 m, assuming a Contributing Drainage Area (CDA) of 500 ha to define the perennial water streams, and a CDA of 15 ha to define manuscript submitted to Water Resources Research Acknowledgments O. G. -F. was funded by the  Spanish Ministerio de Universidades  under application  33. 50. 460A. 752 , by the  European Union NextGenerationEU/PRTR  through a contract  Margarita Salas  from the  University of Vigo , by the  IGE lab  and by the  French Ministry of Environment (Direction Générale de la Prévention des Risques Ministère de la Transition Ecologique et Solidaire)  within the multirisk agreement  SRNH-IRSTEA   2020  ( Action TorRex ). G.
Naves, J. , Rieckermann, J. , Cea, L. , Puertas, J. , & Anta, J. (2020). Global and local sensitivity analysis to improve the understanding of physically-based urban wash-off models from high-resolution laboratory experiments. Implementation of a GPU-enhanced multiclass soil erosion model based on the 2D shallow water equations in the software Iber Science of the Total Environment, 709 , 136152. Nord, G. , Boudevillain, B. , Berne, A. , Branger, F. , Braud, I. , Dramais, G. ,. oth-Luis Cea 1 , Orlando García-Feal 1,2 , Guillaume Nord 3 , Guillaume Piton 3 , Cédric ers (2017). A high space-time resolution dataset linking meteorological forcing Legoût 3 and hydro-sedimentary response in a mesoscale mediterranean catchment (au- zon) of the ardèche region, france. Earth System Science Data, 9 (1), 221-249.
1 Universidade da Coruña, Water and Environmental Engineering Group, Center for Technological Nord, G. , & Esteves, M. (2005). PSEM 2D: A physically based model of erosion pro-Innovation in Construction and Civil Engineering (CITEEC), Campus de Elviña, 15071 A Coruña, Spain 2 Centro de Investigación Mariña, Universidade de Vigo, Environmental Physics Laboratory cesses at the plot scale. Water Resources Research, 41 (W08407). (CIM-EPhysLab), Campus Auga, 32004 Ourense, Spain Nord, G. , & Esteves, M. (2007). Evaluation of sediment transport formulae and detachment parameters in eroding rills using PSEM2D and the Water Erosion Prediction Project (WEPP) database. Water Resources Research, 43 (W08420), 1-14. Journal of Geophysical Research: Earth Surface, 128 (1), e2022JF006846. Pasquero, C. , Provenzale, A.
Since the majority of colonies are not surveyed by ground-based observations, satellite-based surveys provide the bulk of available datasets for population size estimation. As the resolution of satellite imagery has improved over time, satellite-based surveys hold the greatest potential for estimating global populations and detecting trends  (5, 22, 23). However, even at the highest resolution currently feasible, which is on the order of 0. 5 to 1 m/pixel, satellite-based surveys cannot yet provide measurements at the individual level, but rather estimates of the number of animals based on the area occupied by the colony. The viability of using colony area to estimate abundance suffers from uncertainties introduced by the imaging process (satellite off-nadir, sun azimuth, and sun elevation  (22) ), the conversion of colony areas to numbers of individuals, and large fluctuations of colony occupancy due to the species' phenology.
Accordingly, the model requires a critical temperature as a further model parameter. Specifically, the critical temperature corresponds to the apparent temperature at which the average density of the animals within a colony reaches half of the maximum packing density in a dense huddle of 12. 8 animals per m 2. To obtain the windchill model parameters, we manually select the colony covered area from ground-based images of Pointe Géologie and Atka Bay (13, 28) using the image annotation software Clickpoints  (29) , project the resulting polygons to the top view projection  (30) , and calculate the total occupied area in m² (Fig. 5A  & 5B ). From this, we compile a dataset of 538 measurements of colony area, number of individuals (chicks and adults), and meteorological variables (temperature, wind speed, solar radiation, and humidity) acquired at each colony between September and December of three (AB) and four (PG) seasons.
5H. Colony densities (average number of individuals (adults plus chicks) per area) for Atka Bay and Pointe Géologie are calculated from individual counts (weekly resolution) divided by colony area for each of the images. For time points where we had area measurements but not a corresponding individual count, we linearly interpolated the value from the two nearest available counts. We extended our dataset of colony area with satellite image based measurements of colony area from Coulman Island (12 days), Atka Bay (19 days), and Stancomb-Wills Glacier (18 days), between September 10 and December 11, 2011 published in  (22).
Meteorological data The meteorological data for Pointe Géologie and Atka Bay (seasons 2018-2020) were recorded every minute by the meteorological observatory at Dumont d'Urville station (operated by Météo France) and the meteorological observatory at Neumayer station. The meteorological data for Coulman Island, Atka Bay (season 2011), and Stancomb-Wills Glacier stem from the ECMWF model "The ERA5 global reanalysis", which provides 1h temporal and 1km² spatial resolution  (31)  and is available online (https://www. ecmwf. int/en/forecasts/dataset/ecmwf-reanalysis-v5).
Acknowledgements This work was supported by the  Deutsche Forschungsgemeinschaft (DFG)  in the framework of the  priority programme 1158  " Antarctic Research with comparative investigations in Arctic ice areas " by grants ( FA336/5-1 ,  ZI1525/3-1 ,  ZI1527/7-1 ), by the  Institut Polaire Français Paul-Emile Victor (IPEV)  within the framework of the Project  137-ANTAVIA , by the  Alfred-Wegener-Institut Helmholtz-Zentrum für Polar-und Meeresforschung (AWI)  within the framework of the Projects SPOT and MARE, by the  Centre Scientifique de Monaco  with additional support from the  LIA-647  and  RTPI-NUTRESS (CSM/CNRS-UNISTRA ), by the  Centre National de la Recherche Scientifique (CNRS)  through the  Programme Zone Atelier Antarctique et   Terres Australes (ZATA). We thank  Météo France  for the meteorological data.