dataesr/scientific-paragraphs-categorization huggingface dataset:
g. , whether it was nearby weather station data, scene temperature logger data, or some form of regression analysis based on these two sets of data. Challenges exist with each method, and there are numerous factors that may affect the developmental patterns within the parameters of the influence of temperature  [9]. 2. Published developmental datasets based on laboratory studies accurately reflect developmental patterns in the nature of the insect evidence collected: The vast majority of published and accepted insect developmental datasets have been derived under laboratory conditions. These conditions usually applied a range of temperature profiles (constant or with daily variations), controlled humidity and specified light: dark cycles. When developing in a natural environment, none of the abovementioned factors are controlled, and can affect development accordingly  [10] [11] [12].
In such instances, carrion-colonizing arthropods are limited in their access to the remains, often only gaining access after the remains have been discovered or exposed by the elements or by scavengers. In such instances particularly, the entomological evidence obtained provides details regarding the period of environmental exposure, provided the remains have always been in the conditions of their discovery, but cannot provide more specific information regarding a time frame of the decedents' death. 7. Faunal succession patterns on, in and under the body can be used in estimating colonization intervals: While faunal succession patterns are somewhat predictable  [22] , they are seasonally and environmentally-dependent, and depend largely on the faunal species present in an area  [23, 24]. However, precise estimates of exact species present and their arrival patterns at remains cannot be determined without conducting field trials in many different environments, and creating a database of these findings, which is an unrealistic task and not necessarily reproducible outside of an experimental framework.
With the advent of molecular identification techniques, such as DNA barcoding, arthropod identification, especially that of insect fragments, has become easier  [36] [37] [38] [39] [40]. However, despite the vast number of researchers using databases such as GenBank, errors in gene sequences still exist, and are being continuously detected and corrected. One of the most important benefits of using techniques such as DNA for identification is the accurate differentiation of morphologically and behaviorally similar species (provided that the corresponding developmental data set are available), such as Lucilia cuprina (Wiedemann) and L. sericata (Meigen)  [41] , or Hemilucilia segmentaria (Fabricius) and H. semidiaphana (Rondani) (all Diptera: Calliphoridae)  [37]. While these species are behaviorally and morphologically similar, they differ in their developmental patterns, so accurate identification is important to provide a reliable estimate of colonization periods  [42].
References and Citations Selected References included in the case report should reflect the locality of the entomological evidence as far as possible, as well as being relevant to the species identified. Key aspects to consider when compiling/utilizing references include: 1. Locality: Where applicable, the datasets used should be based on insect populations as close to where the evidence was collected as possible. In cases where no local datasets are available, the practitioner should include a statement indicating that local data were not available. It must be noted, however, that developmental patterns by geographic separation may not always differ, and, in some cases, are comparable irrespective of location  [34, 43].
2. Species specificity: Where available, datasets pertaining to the actual species identified should be used. When this is not possible, it is preferable to exclude these species from analysis rather than use a dataset for a closely related species. However, the exclusion of such data is at the discretion of the practitioner, provided they maintain transparency regarding their findings, as sometimes it might be better to guide an investigation with a much more general conclusion based on data from a related species, suitably qualified with regard to accuracy, than to provide no conclusion. In such cases, the practitioner may refer to a generalized larval life cycle of the organisms concerned, or datasets for closely related species, to indicate a possible time of colonization estimate based on prevailing conditions.
Since nests need to be destroyed upon recording to avoid double counting in the following years, the first year of data assessment was omitted. Hence, this study includes 31 years of data. Nests were recorded while walking 30 m-wide transects on flat terrain, and 20 m-wide in rugged topography. High quality aerial photography was used to divide the study region into grid cells and ensure that the same census effort was devoted to each cell  (Sittler 1995). The same aerial photography was then used to assign nest records to a specific cell. For that, the study region was divided into squares of ca. 250 × 250 m. This methodology gives indices on relative abundance, habitat use and spacing patterns during winter  (Sittler 1995).
Data processing Answering our research questions required the revision of the nest data (see Supporting information) and the creation of a digital version of the above-mentioned grid. GIS-based data management and extraction was implemented with ArcMap 10. 7. 1. First, all aerial (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. The copyright holder for this preprint this version posted January 30, 2021. ; https://doi. org/10. 1101/2021. 01. 24. 427984 doi: bioRxiv preprint 9 photographs were georeferenced with a high-resolution (10 m) satellite image (see Supporting information) from the Copernicus Mission (Copernicus Sentinel data 2017). Next, a new digital grid was created with the fishnet tool that matched the analogue one in a bestfit scenario.
2018). Elevation units are referenced to the WGS84 ellipsoid by the ArcticDem project  (Porter et al. 2018). We used the EGM2008 geoid undulation model to convert ellipsoidal height to mean sea level by using the coordinates of the research area (72. 5°N; 24°W) as input variables  (Pavlis et al. 2012). With the elevation and the digital grid in place we could extract topographic and vegetation data for each patch using common GIS tools. Slope was calculated using the Spatial Analyst toolbar and the digital elevation model. Elevation and slope were then calculated for each patch by taking the mean of all raster cells in that patch (see Supporting information). Vegetation data was derived from a geo-ecological study of the study region  (Rau 1995 )(Supporting information). This study analyzed vegetation types at the end of the growing season in early August.
Experimental design and data collection As part of the ongoing reforestation plan, in 2014, a total of 36 plant species were planted between November 2014 and February 2015 at the Buffelsdraai Landfill Conservancy reforestation site. Since the site is characterised by dry and moist microhabitats, the planting density was 2000 seedlings/saplings per ha in the moist microhabitat while 1000 seedlings/saplings per ha were planted in the dry microhabitat. The actual number of the individuals planted per species is unknown. In February 2015, a field survey at the site was undertaken to identify the most dominant indigenous pioneer tree species planted in both the dry and moist microhabitats for the long-term monitoring study. The most dominant species that were planted in both microhabitats were Bridelia micrantha (Hochst.
The basal stem diameter (5 cm above root collar, hereafter termed stem diameter) was measured using a caliper at 10 cm above the ground. Canopy width was measured in two directions (north-south and east-west) through the middle of the canopy  (Li et al. , 2014). Since this was an opportunistic study lacking a control (i. e. , irrigated seedlings/saplings), annual growth rate (tree height) data of the study species under non-drought conditions were extracted from the literature  (Johnson and Johnson, 2002)  for comparison with the annual seedling/sapling height growth rate data measured under drought conditions in the study. In the absence of control, especially during sporadic drought events, data from studies that reported species growth rates and survival under non-drought conditions are used as controls (e.
Data analyses Annual seedling/sapling stem diameter, height, and growth rates were calculated as the difference between February 2015 and March 2016 values. Canopy width (CW) was calculated as the average of the two canopy diameter measures. The annual CW growth rate was calculated as the difference between the crown diameter in 2015 and 2016. Generalized linear models (GLMs) using the Gaussian family with an identity link function (Mass package;  Venables and Ripley, 2002)  were applied to analyse the effect of microtopographic positions (dry and moist microhabitats) and species on (1) stem diameter, (2) height, and (3) CW growth rates. The 'car' package  (Fox and Weisberg, 2019)  was used to generate the F stat with P-value outputs. For each species in both the dry and moist microhabitats, the survival (%) was calculated as [(number of individuals in 2015 À number of individuals in 2016) / number of individuals in 2015] £ 100.
Acknowledgments This research was supported by  eThekwini Municipality  through the  Durban Research Action Partnership: Community Reforestation Research Programme , and by the  South African Research Chairs Initiative of the Department of Science and Technology and National Research Foundation of South Africa. The authors also thank the  University of KwaZulu-Natal  for research support. We are indebted to the  Buffelsdraai Landfill Conservancy  staff for the logistics and technical support. Vuyani Cele ,  Angelique Lazarus ,  Rerani Ramaano  and  Jabulile Vumisa  are thanked for field assistance. We would like to thank the anonymous reviewer for their useful and positive comments that greatly improved the manuscript.
Particularly, NH3 as a reagent gas improves amine identification in positive mode (PCI) and esters, alcohols, carbonyls, amides, carboxylic acids and nitriles in negative mode (NCI). The combination of both EI and CI mass spectrum analysis improves molecular identification, thanks to the detection of molecular, deprotonated or protonated ion of highest intensity and fragment formula assignments. The EI and NCI NH3 combination allows formula assignments up to 94 % of our database with limit of detection up to 7 ppm. This procedure has been validated for untargeted GC-FT-Orbitrap-MS analysis of VOC coming from the processing of cometary ice analogs.
Chemicals, database construction and LOD measurement. 110 chemicals were purchased from Sigma-Aldrich, Aldrich, Fluka, Fisher, Carlo Ebra, Acros and Riedel-de Haën with the purity of 80 to 99. 9 % (see supplementary information TS1 for details). They were chosen according to their astrophysical interest  [10, 22, 23]  contains #C = 0 to 6, #O = 0 to 3 and #N = 0 to 1 from 12 different chemical functions (see supplementary information Figure  S1 ). The 500 mg/L standards mixtures methanol (Fisher Chemicals, 99. 99 %) solutions were designed to avoid co-elutions. EI identifications were confirmed with fragment comparison to NIST MAINLIB database (267376 spectra). CI spectral identification was done using retention time (RT). NIST HR Databases were created using Deconvolution Plugin 1. 3 for TraceFinder 4.
GC analysis was performed using a Rxi®-Sil624MS column (60 m × 0. 25 mm i. d. × 1. 4 μm d. f. from Restek) for all the analysis. Helium (scientific 6. 0 from Linde-France) carrier flow was fixed to 1 mL/min with vacuum compensation, Methane (Alphagaz, Air Liquide) or ammonia (Alphagaz, Air Liquide), in positive (PCI) or negative (NCI) modes were used. Tuning of PCI CH4 and NCI CH4 was done using FT43 and corresponding tune files were used for PCI and NCI NH3 analysis. EI source was heated up to 250 °C, PCI CH4 to 350 °C with 2. 0 mL/min gas flow, NCI CH4 to 200 °C with 1. 5 mL/min, PCI NH3 to 300 °C and 1. 5 mL/min and NCI NH3 to 150 °C with 2. 0 mL/min. Except when specified electron voltage was set at 70 eV (otherwise 10, 30, 50 eV). Data were recorded with resolution m/Δm = 60 000 at m/z = 200 Th (from 142 360 at m/z = 45.
02146 Th to 87 795 at m/z = 116. 08373 Th, see supplementary information TS2 and TS3). Automated gain control (AGC) target was 106, maximum precursor ion injection time (ITmax) was 200 ms and maximum IT C-trap parameters. MS transfer line was set at 250 °C. For gas injections the mass spectrometer recorded within the mass range m/z = 30 to 200 Th, while liquid injection mass range was 40 to 200 Th. All data analyses were monitored using Qual Browser Thermo Xcalibur 4. 1. 31. 9.
RESULTS The combination of retention time (RT) and low resolution mass spectrometry has been sufficient to identify 33 VOCs from methanol irradiated ice  [10]. Analytical improvement of such analysis shows that many compounds remain unknown  [12]. Extending the database containing such specific compounds is highly expensive and time consuming. In this context, stoichiometric formula assignments using HRMS improve identification ,  [21, 25, 26]  and optimize standard analysis. The first step to obtain this information is to optimize high mass ions detection on a set of standard molecules.
Qualitative analysis. To exhaustively characterize remaining unknown compounds after database comparison, we propose a workflow (Figure  4 ) to combine GC-HRMS analysis from different ionization methods. Step 1 to step 3 are classical steps for compound identification in GC-MS: acquisition, step 1, mass trace deconvolution and peak detection, step 2, and mass spectra extraction and library search, step 3. Retention time (RT) alignment, step 4, is a key step to compare different ionization modes using characteristics found in the present study. We must improve this step because no internal standards can be used for cometary ice analogs VOCs analysis. Finally, steps 5 and 6 aim to restrict possibilities before standard analysis confirmation step 7.
Simple ionization mode GC-HRMS analysis. After recording the chromatographic data using different ionization sources, step 1, a list of detected compounds with their corresponding identification is separately edited: chromatographic peaks are listed thanks to peak detection on narrow (± 5 ppm) extraction of mass traces, step 2. The corresponding mass spectra and RT are compared with the existing database. Mass spectrum comparison is obtained thanks to the mass spectrum similarity index defined as = 1 - ∑ | ⁄ ⁄ | ⁄ ∑ | ⁄ ⁄ | ⁄ × 100 Eq. 1  [30] , with Iu(m/z) the intensity of the unknown at exact mass m/z and Il(m/z), the intensity of the compound referenced in the library at same exact m/z, and the RT difference. As shown previously using reference spectra, NCI NH3 is used for esters, alcohols, carbonyls, amides, carboxylic acids and nitriles analysis.
Standard identification. The classical identification process was monitored using the database (see supplementary information Table  S12 ) with the SI (Eq. 1) indicator for high resolution mass spectra (Δm/m < 5 ppm) and retention time comparison. This allowed the identification of dioxygen (2), carbon dioxide ( diethyl ether  (11)  and methyl acetate (22) (Figure  5 ). Identifications are commonly confirmed using both EI and NCI NH3 ionization methods. The only exception is dioxygen (2) that has been only detected by EI because of its better sensitivity, and methyl acetate  (22)  because NCI NH3 has better sensitivity for esters than EI. Monitoring both EI and NCI NH3 analysis enables deconvolution of coeluting species while extracted ion chromatograms cannot distinguish them. For instance, Peak@9.
03347 Th and 59. 04920 Th at the right RT after comparison with the analysis of the products of a Williamson synthesis. Finally, the only ion present in the EI-MS of Peak@9. 01 is [C3H7O] + (m/z = 59. 04922 Th, DBE = 0. 5). This implies that it is an ether with M = C3H7O-R with unknown alkyl chain R with fragment 59 which must be more intense than fragments 31, 43 or 49. Comparing RT with the one recorded in our database and the intensity of fragment 59 intensity relative to the three others fragments in mass spectra from the NIST database 2-methoxy propane seems to be the most probable identification of Peak@9. 01.
The best compromise for sensitivity and easy molecular formula identification consists in combining EI with NCI NH3, for all compounds, except amines, alkanes and ethers, with an accuracy of formula detection of 94 %. Semi-quantitative analysis shows that NCI NH3 and EI have similar LODs up to 7 ppm (database of 110 compounds containing 1 to 6 carbon atoms Table  S1 ). In the case of amine identification, the ideal combination is EI with PCI NH3, while only EI can ionize alkanes and ethers. From those results, a workflow (Figure  4 ) has been built up using formula identification thanks to high resolution mass spectra and chromatographic normalized TIC comparison of CI with respect to EI. This workflow has been cross-validated on the identification of unknown VOCs generated by the photo-processing of an ice analog of methanol.
Specially, dimethyl ether (4), acetaldehyde (5) and methoxy ethane were identified and validated while propene and 2-methoxy propane were identified without standard analysis validation. Further improvements of this method will be to identify compounds from nitrogen containing cometary ices analogs. S1  and more detailed data are displayed in supplementary information Table  S3 ). barcharts corresponds to electron ionization (EI), green to positive chemical ionization (PCI) using methane (PCI CH4), purple to negative chemical ionization (NCI) using methane (NCI CH4), yellow to PCI using ammonia (PCI NH3) and blue to NCI using ammonia (NCI NH3) (see Table  S2 ). 5 ), removed from the database for cross-validation, were identified using mass spectra qualitative analysis and validated with standard identification.
Figure5. GC-MS chromatograms (TIC) from two analyses (EI at 70 eV and NCI NH3) of VOCs recovered after the warming of an ice of pure methanol irradiated during 76 h concomitantly to the gas deposition. Identifications are labeled by following the strategy described in Figure4. Dioxygen (2), carbon dioxide (3), methanol (8) , methyl formate (6), ethanol (12) , diethyl ether (11)  and methyl acetate (22)  were detected using library search and confirmed with NCI NH3 mass spectra identification. Coupling EI and NCI NH3 analysis enable coelution separation (ethanol (12) , diethyl ether (11)). Dimethyl ether (4) and acetaldehyde (5), removed from the database for cross-validation, were identified using mass spectra qualitative.
ACKNOWLEDGMENT G. D. and T. J. are highly grateful to  Y. Guillaneuf  and  C. Lefay  from  Institut de Chimie Radicalaire, UMR CNRS 7273  for their help for the synthesis of methoxy ethane. G. D. thank the  Région Sud/Provence-Alpes-Côte d'Azur , the  Agence Nationale de la Recherche  ( RAHIIA_SSOM ,  ANR-16-CE29-0015  and  VAHIIA ,  ANR-12-JS08-0001 ), the  Centre   National d'Etudes Spatiales from its exobiology program , and the  Centre National de la Recherche Française (CNRS, "Physique et Chimie du Milieu Interstellaire" (PCMI)  and  "Programme National de Planétologie" (PNP) programs)  for their financial support. T. J. thank  ENS Lyon  for foundings.
Acknowledgements This work received funding from the  EU Horizon 2020   NOVAMAG  project ( NMBP 23-2015 , Grant Agreement No. 686056 ) and from the  Junta de Castilla y León  and the  European Social Fund-Youth European Initiative  Grant  UBU-15 -A. We thank  Marc De Bortoli , from  ARELEC Magnets  and  Magnetic Systems , for kindly providing us with the commercial NdFeB alloy powders.
GPC (Gas Permeation Chromatography) GPC measurements were performed according to the method described by Laun et al. [33]. Samples were dissolved in 1,1,1,3,3,3-hexafluoroisopropan-2-ol (HFiP) and then injected in a Water 2695 chromatograph at a flow rate of 1 ml/min. A differential reactive index detector was used to perform detection and data was processed with PSS WinGPC unity v7. 5 SEC software. The calibration was based on poly(methyl methacrylate) standards supplied by PSS GmbH Mainz, Germany, with molar mass ranging between 800 and 1,600,000 g mol -1 , and the calibration curve was adjusted with a 5th order polynomial.
Acknowledgement The authors would like to thank the  Merlin Microplastics  project funded by  Ifremer. Financial support from  Région Nord Pas-de-Calais and European FEDER  for the SAXS laboratory equipment is gratefully acknowledged. Quentin Deshoulles was supported by a  Région Bretagne  fellowship. We gratefully acknowledge the contributions of  Christophe Peyronnet  and  Mickael Premel Cabic (Ifremer)  for their technical assistance and advice.
Livestock farming occupies 57% of agricultural area in France and has contrasting impacts on the environment. Studies have analyzed relations between livestock farming and soil organic carbon (SOC) content, but the influence of livestock farming on soils is difficult to perceive at a large scale. The objective of this study was to increase understanding of impacts of livestock farming on soils that receive livestock manure depending on different initial levels of SOC content, at cantonal level. To this end, we used French soil and agricultural databases to analyze relations between livestock farming practices and SOC content. We used statistical data calculated from the French soil test database for the periods 2000-2004 and 2010-2014. For livestock farming practices, we used data from the French agricultural census of 2000 and 2010, and for spreading of livestock manure, data from the French program to control pollution of agricultural origin (2002)(2003)(2004)(2005)(2006)(2007) and data from the French Livestock Institute.
The novelty of our large-scale analysis is to differentiate the origin of livestock manure (herbivore or granivore) and the type of crop on which it was spread (crops or grasslands). Statistical analysis was performed at the cantonal scale for France using the method of generalized least squares. We show for the first time that, at the national scale, spreading of livestock manure influences SOC content and dynamics significantly. Our results also show the importance of the nature of the manure; solid manure increases SOC content, unlike liquid manure. Spreading herbivore manure on crops increases SOC content, but spreading granivore manure may decrease it. Livestock manure spread on grasslands has no significant effect on SOC content, possibly due to under-representation of grassland soils in the soil database.
2019). Thus, the objective of this study was to increase understanding of impacts of livestock farming on soils, specifically the effect of LM spreading on SOC content. This study will investigate the impacts of livestock systems on SOC via a statistical approach using a large dataset based on census data, soil properties, climate indicators, and farm practices available at the French level. We also tested whether the impacts of livestock systems vary depending on different initial levels of topsoil quality, when soils are either initially poor or rich in SOC content, at the cantonal level in France.