dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgement Data acquisition at FR-Lam and FR-Aur are mainly funded by the  Institut National des Sciences de l'Univers (INSU)  through the  ICOS ERIC  and the OSR SW observatory (https://osr. cesbio. cnrs. fr/). Facilities and staff are funded and supported by the  Observatory Midi-Pyrenean , the  University Paul Sabatier of Toulouse 3, CNRS  ( Centre National de la Recherche Scientifique ),  CNES  ( Centre National d'Etudes Spatiales ),  INRAE  ( Institut National de Recherche en Agronomie et Environnement ) and  IRD (Institut de Recherche pour le Développement). We are grateful to  Franck Granouillac ,  Baptiste Lemaire ,  Nicole Claverie  and  Bartosz Zawilski  for their technical support, advice, and valuable assistance in the field and site management, respectively. Our special thanks go to  Mr.
First, we highlighted the main agro-environmental gradients structuring weed communities according to their combination of traits, using multivariate analysis (RLQ). Then, two different aspects of the functional structure of the weed communities were assessed using trait values from databases: Community Weighted Means (CWM) which is the mean value of traits of weed communities, and Community Weighted Variance (CWV) which is the variability of these trait values within the community. We used mixed linear models to test the effects of the explanatory variables on the CWM of weed communities. Secondly, we evaluated the seasonality and weed management practices effects on CWM within each wine-growing region. Thirdly, we tested if CWV were significantly impacted by weed management practices and seasonality using a null model approach to disentangle the effect of functional variance from the effect only due to species richness  (Perronne et al.
Three different weed management practices were applied on rows and inter-rows in these vineyards: chemical weeding, tillage and mowing. As mowing on rows was only exceptionally applied in our dataset (applied in two plots in Rhône, representing 7 floristic surveys), we decided not to consider this variable. At the global dataset scale, chemical weeding concerned one third of the inter-rows and 90% of the rows. Farmers of the vineyard network used pre-emergence and postemergence herbicides. Active ingredients of post-emergence herbicide were mostly glyphosate. Pre-emergence herbicide was mostly constituted of oryzalin. Tillage was applied on one third of the inter-rows and 17% of rows. Tillage was mostly superficial (mean of 12 cm and ranging from 5 cm to more than 20 cm). Mowing concerned one third of interrows.
Traits data Six plant traits were selected to capture plant responses to environmental variations and weed management practices. Three traits of the Leaf-Height-Seed (LHS) strategy scheme were selected  (Westoby, 1998) : (a) specific leaf area (SLA) which is the light-catching area deployed per unit of previously photosynthesized dry mass, is related to the speed of resources acquisition  (Wright et al. , 2004) , (b) maximum height which expresses the possible amount of growth in an undisturbed environment and which is related to light and nutrient acquisition  (Westoby et al. , 2002) , (c) seed mass which represents the 'colonisation-competition' trade-off  (Moles and Westoby, 2006)  illustrating two strategies: 'producing a large number of small seeds, each with low establishment ability and high colonizing capacity' and 'producing fewer, larger seeds, each with a higher chance of successful establishment'  (Westoby et al.
, 2002). Three other traits related to persistence and regeneration in disturbed habitats were selected: (d) flowering onset, (e) flowering duration and (f) lateral spread ability. Lateral spread is a qualitative trait which represents species abilities to develop horizontally (species with rhizomes or forming tussocks); it is rated as followed: '1', therophytes; '2', perennials with compact unbranched rhizomes or forming small tussocks (less than 100 mm in diameter); '3', perennials with rhizomatous system or tussocks reaching from 100 to 250 mm; '4', perennials reaching diameter of 251-1000 mm. The trait values were extracted from different databases: the LEDA Traitbase for SLA  (Kleyer et al. , 2008) , Flora Gallica for maximum height  (Tison and De Foucault, 2014) , the Seed Information Database (SID) for seed mass (Royal Botanic Gardens Kew, 2021), Baseflor for flowering onset and duration  (Julve, 1998)  and lateral spread from  Hodgson et al.
Mixed linear models of CWM To evaluate the relative importance of region, temporal variables and weed management practices effects on CWM, we constructed mixed linear models for each CWM ('lmer' function of lme4 package  (Bates et al. , 2015) ). We defined two random effects in each model: the vineyard plot identity and the year of floristic survey. Seed mass, lateral spread and flowering duration were logarithmised to validate hypotheses of linear models. Prior to model construction, the collinearity of explanatory variables was investigated using the variance inflation factor (VIF < 5). Model selection was performed using a backward step selection procedure based on AIC  (Burnham and Anderson, 2004). We calculated the explained variance of each covariate as the percentage of variance additionally explained when each variable was added one by one to the model.
In order to detect a general pattern of community structure regardless of the spatio-temporal and agronomic characteristics, we tested if ES was overall different from 0 using a two-tailed Wilcoxon signed-ranks test  (Bernard-Verdier et al. , 2012; Jung et al. , 2010). To investigate the impact of the weed management practices gradient on CWV, we used the same procedure as for CWM. Within each wine-growing region, we tested the significance of correlations between effect sizes of CWV and the coordinates of the plots on the first two axes of the PCA, using Spearman's rank correlation. All the statistical analyses were performed with R (3. 6. 2 version). All multivariate analyses (RLQ, PCA) were conducted using ade4 package  (Chessel et al. , 2004).
, 2004). Due to higher temperatures in autumn, more thermophile species can flower and produce seeds during this season in the Mediterranean region (e. g. Dittrichia viscosa, Heliotropium europaeum, Sorghum halepense, Tribulus terrestris). Thus, on average, this could explain the later flowering communities of the Languedoc. Moreover, soil characteristics are also encompassed in the regional effect. In our dataset, vineyard plots located in the Rhône had more fertile soils (high soil organic content) compared to vineyard plots of the other regions (Table  A. 1). Fertile soils are usually associated with weed communities with high photosynthetic assimilation rates, high SLA, high height at maturity and low seed mass  (Garnier et al. , 2016). However, only flowering duration was significantly different in the Rhône region while the other traits were not significantly different from the other regions.
Limits and perspectives Trait-based approaches are promising a better understanding of the functional shaping of weed communities by weed management practices. In our study, the lateral spread ability of communities was certainly one of the major response traits. Other traits, absent from the databases, such as the presence of epicuticular wax on leaves or seed coat thickness, might be response traits of interest to include in such studies where herbicide pressure is an important filter  (Gaba et al. , 2014). However, these specific traits are still lacking in trait databases. The use of pluriannual database of floristic surveys such as the Biovigilance network is an asset when considering the variations between years due to changing climatic conditions for instance. Moreover, the wide geographic range of our study allowed us to analyse the different weed management practices within regions and different pedoclimatic contexts.
At this large-scale of analysis, one drawback is that we had to fragment the dataset to control the weed management and pedoclimatic contexts interactions therefore limiting the statistical power of our dataset. Another disadvantage is that it is difficult to use measured trait values. We therefore used database trait values based on the assumption that the ranking of species according to their trait values is stable across environments ('stable species hierarchy',  Kazakou et al. , 2014)  as interspecific variability is higher than intraspecific variability. A recent study has shown that this hypothesis was largely valid in vineyards  (Garcia et al. , 2020). Another point is that our results demonstrated that weed management practices explained 19% of variations of functional structure of weed communities.
Acknowledgement This research was supported by  Occitanie Region  ( Arrêté modificatif N • 19008795 / ALDOCT-000660 Subvention d'investissement, Allocations de recherche doctorales 2019 ) and the  Office Français de la Biodiversité  (ECOPHYTO II: Axe 2 -Action 8 and 9, N • SIREPA: 4148) as part of the  SAVING  project: Spatio-temporal dynamics of weed species communities in response to soil management practices in vineyards and consequences for grapevines: transition to zero glyphosate management. We would like to thank all winegrowers who provided management information and access to their farms. Thanks to the  Biovigilance Flore  network including all the people from SRAL and FREDON who performed the surveys,  Nicolas André  ( FREDON Occitanie ),  Jacques Grosman  ( DGAL, SRAL Rhône-Alpes ), and  Olivier Pillon  ( SRAL Champagne ) for data management at the regional level, and the  Ministry of Agriculture  for funding the monitoring.
Warm thanks to  Margot Puiraveau  who gathered the dataset in 2015. The study utilised data provided through the TRY initiative on plant traits (http://www. try-db. org). The TRY initiative and database is hosted, developed and maintained by  J. Kattge  and  G. Beonisch  ( Max Planck Institute for Biogeochemistry, Jena, Germany ). TRY is currently supported by  DIVERSITAS/Future Earth  and the  German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig. The authors have no conflicts of interest to declare.
AMS instrument The chemical composition of the non-refractory submicron particulate mass was measured in real-time by an Aerodyne high resolution time-of-flight-aerosol mass spectrometer (HR-ToF-AMS) only in Finokalia sampling station. The concentrations of the major PM 1 components were measured every 3 min using both mass spectrum (MS) and particle time of flight data (pToF). In this study only V-mode data were obtained. The heater was operating at 600 °C for the aerosol thermal desorption and the tungsten filament for electron ionization was at 70 eV. All HR-ToF-AMS data were analyzed using the standard AMS software toolkit (SeQUential Igor data Re-tRiEvaL; SQUIRREL v1. 56D), while the high-resolution data were processed using the Peak Integration by Key Analysis (PIKA v1. 15D, available at: http://cires1.
colorado. edu/jimenez-group/wiki/index. php/High_ Resolution_ToFAMS_Analysis_Guide) software, within Igor Pro 6 (Wave Metrics). A time-dependent collection efficiency has been applied to our HR AMS data during the campaign. This collection efficiency was calculated according to the algorithm of  Kostenidou et al. (2007) , which combines the AMS PTOF mass distributions and the SMPS volume distributions and was on average 0. 64.
For this, we estimate the fraction of NVCs in PM 1 , considering the size segregated measurements of NVCs during dust events occurred in the area (Table  S6 ). The estimated fraction of the species in PM 1 was applied to the PM 2. 5 data and was used to estimate the pH in PM 1 particles using ISORROPIA-II (Fig. 2d ). The lower pH in PM 1 particles (pH = 1. 61) at the begging of event implies that the solubilization of dust was likely complete in the fine particles, while the modest variability in pH in PM 1 particles during the dust episode indicate an underlying acidification that may be underway. Nevertheless, a lack of more size-Fig. 4. Concentration of SRP measured with the PILS-LWCC system in PM 2. 5 atmospheric particles, sulfate measured with AMS in PM 1 atmospheric particles and PM 10 mass during the sampling in Finokalia (11th-13th May 2016).
Acknowledgements KV acknowledges the financial support from the  European Union 's  Horizon 2020 research and innovation programme  under the  Marie Skłodowska-Curie  grant agreement No  707624. KV and NM acknowledge financial support from the  ACTRIS, Research Infrastructure Action  under the  7th Framework Programme  (Grant Agreement No  262254 ). AN acknowledge support from project  PyroTRACH  ( ERC-2016-COG ) funded from  H2020-EU. 1. 1. - Excellent Science -European Research Council (ERC) , project  ID 726165. KV and AN acknowledge the  Swiss National Science Foundation  project  192292 ,  Atmospheric Acidity Interactions with Dust and its Impacts (AAIDI ). MT, GK, PZ and NM acknowledge support by the project " PANhellenic infrastructure for Atmospheric Composition and climatE change " ( MIS5021516 ), which is implemented under the Action " Reinforcement of the Research and Innovation Infrastructure ", funded by the  Operational Programme "Competitiveness, Entrepreneurship and Innovation"  ( NSRF 2014-2020 ) and co-financed by Greece and the  European Union  ( European Regional Development Fund).
The shaded area shows the full range of modeled atmospheric radiocarbon activity variability that can be accounted for by radiocarbon production rate estimates alone (i. e. , with no additional carbon cycle changes;  Dinauer et al. , 2020). The dashed gray line shows modeled atmospheric radiocarbon activity based on production rate changes given by averaged estimates of relative paleomagnetic intensity (RPI)  (Dinauer et al. , 2020). SKINNER AND BARD 10. 1029/2020RG000720 11 of 64 and 2020 iterations in a series of updates  [Reimer et al. , 2004 [Reimer et al. , , 2009;; Stuiver et al. , 1998] ) represent estimates of the history of past atmospheric radiocarbon activity that have been derived using a Bayesian statistical approach  (Niu et al. , 2013) , applied to selected radiocarbon datasets  (Reimer et al.
, 2019; Hogg et al. , 2016). The Intcal radiocarbon datasets incorporate tree-ring, lacustrine, marine and speleothem radiocarbon data, on independent calendar ages, which have been "corrected" for estimated relative isotopic enrichments to give atmospheric radiocarbon activities, and therefore an atmospheric radiocarbon-versus calendar age "calibration curve. " At the time of writing, the Intcal13 calibration curve has just been superseded by the Intcal20 iteration  (Reimer et al. , 2020). Most recent work in this field will have made use of the Intcal13 atmospheric curve; where possible in the figures that follow we have re-referenced marine data to the Intcal20 atmospheric curve. Notably, Intcal20 differs very little from Intcal13 back to ∼27 ka BP (see Figure  4 ); however, it does represent a less "smoothed" record, and therefore contains a greater amount of very high-frequency (annual to decadal) variability.
Radiocarbon Ventilation Patterns at the LGM Despite the emerging clarity regarding marine carbon cycling at the LGM  (Galbraith & Skinner, 2020) , major challenges remain to be addressed insofar as radiocarbon is concerned. One outstanding challenge is to narrow the scatter in marine radiocarbon ventilation age estimates for the LGM, and in particular to better constrain their spatial patterns. To some extent this is a question of improving high-quality data coverage, allowing for example, vertical-, intra-and inter-basin gradients to be constrained with greater precision. Figure  11  shows a global spatial interpolation of available radiocarbon data, following  Skinner et al. (2017) , indicating a significant "aging" >2,000 m, particularly in the deep Atlantic and Southern Ocean, but also extending into the Indo-Pacific.
, 2015; Marchitto et al. , 2007; Rafter et al. , 2018 Rafter et al. , , 2019)) , but also the South Pacific  (Ronge et al. , 2016 (Ronge et al. , , 2019)) , and Arabian Sea  (Bryan et al. , 2010). These extreme radiocarbon depletions reflect statistical "outliers" in the global LGM and deglacial datasets, in the sense that they differ significantly from the global population  (Skinner et al. , 2017) ; however, they cannot be ignored and currently defy straightforward explanation and integration into a complete theory of LGM and deglacial carbon cycle change. We return to these extreme observations when discussing the last deglaciation, below.
Although millennial-scale fluctuations in atmospheric radiocarbon are resolved with difficulty prior to the last deglaciation  (Gottschalk et al. , 2016) , similar associations between the activation of the thermal bipolar seesaw and pulses in atmospheric CO 2 are also apparent on previous deglaciations  (Barker et al. , 2019) , and during previous glacial cycles  (Gottschalk et al. , 2019; Nehrbass-Ahles et al. , 2020). Millennial carbon cycle perturbations are thus persistent phenomena in the climate system that could play an important role in longer-term climate dynamics  (Barker et al. , 2019; Wolff et al. , 2009). It has been argued that the available deglacial radiocarbon database exhibits sufficient scatter to preclude the unequivocal identification of any trends in global average ocean-atmosphere disequilibrium across the last deglaciation  (Zhao et al.
, 2020) , which show no evidence for extreme radiocarbon depletion in a region that is currently influenced by AAIW (stars in Figure  17f ). As shown in Figure  15 , these data exhibit a similar trend to coral data from the Southern Ocean  (Burke & Robinson, 2012; Chen et al. , 2015; Li et al. , 2020) , consistent with the expected preponderance of southern sourced mode waters in both of these locations, as in the modern ocean  (Fiedler & Talley, 2006). The collected evidence therefore suggests that extreme radiocarbon depletion observed in benthic and planktonic foraminifera at several locations at the LGM and across the last deglaciation most likely reflects the influence of geological carbon sources on the sedimentary environment in which the foraminifera lived or were deposited, rather than the ocean interior per se.
This review benefited from stimulating discussions during INQUA IPODS, PAGES OC3, and Intcal marine working group meetings. L. C. Skinner acknowledges funding from the Royal Society, the Isaac Newton Trust, and NERC grant NE/L006240/1, which supported data collection that has informed this review. E. Bard is supported at CEREGE by EQUIPEX ASTER-CE-REGE and ANR MARCARA.
Three mesocosms were used as controls. HuminFeed ® (HuminTech, Germany) was added to the other three mesocosms (hereafter referred to as +HF mesocosms). The HuminFeed ® was diluted in distilled water before being added to the mesocosms on August 6th (hereafter called d 0) to reach a concentration of 2 mg L À1 (corresponding to the addition of 0. 8 mg L À1 of DOC;  Meinelt et al. 2007 ). HuminFeed ® is a well-characterized substance derived from Leonardite that has been used in several studies to study the impact of increased water coloration on aquatic systems  (Rasconi et al. 2015; Lebret et al. 2018). Sensor setup in the in situ mesocosms and high-frequency data acquisition Each mesocosm was equipped with a set of automated sensors immersed at 1 m depth. The sensors measured biotic and abiotic parameters at high frequency (one measurement every 15 min) during all 15 d of the experiment.
The automated sensors that provided the data used in the present investigation were (i) an oxygen optode (Model 3835, Aanderaa) to measure the DO concentration and oxygen saturation, (ii) an electromagnetic induction conductivity sensor (Model 4319, Aanderaa) to measure the conductivity (and therefore the salinity) and water temperature, and (iii) a spherical underwater quantum sensor  to measure the incident PAR. To ensure the accuracy of the sensor data and to avoid any potential drift, every sensor was calibrated before and after deployment (see Supporting Information).
Statistical analyses We used repeated measures ANOVA (RM-ANOVA) with treatment as fixed factor and time as random factor (linear mixed-effects models with autoregressive process of order 1, lme(Response $ Treatment, random = $ 1jTime, cor = corAR1), nlme package, R software) to test the effects of HuminFeed ® addition during the entire experiment on physical parameters (water temperature, salinity, DLI, nutrient concentrations), metabolic parameters (GPP, R), the Chl-a concentration, and pigment concentrations and ratios. The significance level was set at 0. 05. When the assumptions of the RM-ANOVA could not be met even after transforming the data (logarithmic, square-root or exponential transformations), a nonparametric Kruskal-Wallis test was performed instead. For the abundance of each zooplankton genus obtained on the last day of the experiment, an unpaired t-test comparing the mean abundance in the control and in the +HF treatment was performed.
Acknowledgments We would like to thank  David Parin  for his participation in the mesocosm experiment and for setting up the sensors and performing data acquisition. We would like to thank  Maria Stockenreiter ,  Kiersti Andresen ,  Olav Vadstein  and  Yngvar Olsen  for infrastructural support and Tore and  Katja Alstad  for logistical support at the Sletvik field station of the  Norwegian University of Science and Technology (NTNU). Furthermore, we thank  Robert Ptacnik  for his helpful comments on the previous version of the manuscript. This work and a part of the PhD grant of T. S. were funded under the  AQUACOSM  project, which have received funding from the  European Union 's  Horizon 2020 research and innovation program  ( H2020/2017-2020 ) under grant agreement number  731065.
g. , EPA;  Hixson et al. , 2015). Although several insect families were represented in this global dataset, the insect sample size was rather limited (n < 70), which may explain the large variation observed in insect FA profiles. Insects from both ecosystems have also been compared in regard to their quality as prey for insectivorous riparian predators such as birds, spiders, and bats  (Lam et al. , 2013; Kowarik et al. , 2021; Twining et al. , 2021b). Aquatic insects have been found to be rich in EPA, while terrestrial insects were found to be higher in ALA (e. g. , Hymenoptera and Lepidoptera;  Twining et al. , 2021c). However, several of these previous studies were limited to the breeding seasons of riparian birds reliant on insects  (Twining et al. , 2021b,c; Shipley et al. , 2022)  and thus did not capture additional temporal variation in insect diversity and FA composition.
Unlike previous studies, we sampled aquatic and terrestrial insects within and around a single lake throughout an entire growing season (April to October). We explored (1) differences in FA profiles between aquatic and terrestrial insects, (2) the main FA that drive the potential separation between aquatic and terrestrial insects, and (3) temporal changes in the strength of this FA separation during the growing season. We provide a thorough dataset of aquatic and terrestrial insect FA profiles (FA content per unit biomass), as well as visualize the similarities and/or differences between taxa with respect to 30 quantifiable FA. We predicted that terrestrial insects contain higher contents of C18-PUFA (i. e. , ALA and LIN), whereas aquatic insects contain higher contents of ≥ C20-PUFA (i. e.