dataesr/scientific-paragraphs-categorization huggingface dataset:
Data sample and object definition The analysis is performed with the 2015 and 2016 proton-proton collision data sample produced by the LHC at a centre-of-mass energy of √  = 13 TeV and collected by the ATLAS experiment, corresponding to an integrated luminosity of 36. 1 fb -1  [18]. The data sample was recorded during stable beam conditions, and all relevant ATLAS detector subsystems were required to be operational. The average number of   collisions in the same bunch crossing (referred to as pile-up) in the data sample is 24. 1. Electron candidates are reconstructed from energy deposits (clusters) in the electromagnetic calorimeter matched to reconstructed tracks in the inner detector. Candidates in the transition region 1. 37 < | cluster | < 1. 52 between the calorimeter barrel and endcaps are excluded.
Statistical and datasets The uncertainty related to the size of the data sample (data statistical uncertainty) is obtained by performing the fit while keeping constant all of the nuisance parameters associated with the systematic uncertainties. The data statistical uncertainty obtained by using both the OS and SS selections is ±0. 39 GeV, while the OS sample alone yields ±0. 47 GeV, highlighting the contribution to the sensitivity from the SS sample. The uncertainty due to the limited size of the simulated signal and background samples includes both the impact of the signal samples size on   -dependent templates used for the top mass interpolation, and the uncertainty of the backgrounds due to the limited size of the corresponding MC samples. This includes the multĳet background, which is estimated with a data control sample.
on   [GeV] Stat. precision[GeV] Events / 1 GeV 5000 6000 7000 ATLAS = 13 TeV, 36. 1 fb s OS selection Post-Fit -1 Data SMT ( t t SMT ( t t SMT ( t t Single top from from fake) Other backgrounds ) c / b ) W 10000 Events / 0. 2 8000 ATLAS = 13 TeV, 36. 1 fb s OS selection Post-Fit -1 Data SMT ( t t SMT ( t t SMT ( t t Single top from from fake) Other backgrounds ) c / b ) W 4000 Uncertainty Uncertainty 6000 3000 4000 2000 Statistical and datasets 1000 2000 Data statistics Signal and background model statistics Luminosity Pile-up 0 10 20 30 40 50 60 70 80 90 100 0. 7 0. 85 1 1. 15 Data / Pred. prob = 0. 97 2 χ /ndf = 2. 8 / 9 2 χ 0 Data / Pred. 3 -0. 7 0. 85 1 1. 15 0 χ 2 0. 39 0. 17 < 0. 01 0. 07 1 -prob = 0. 34 0 2 χ /ndf = 28. 3 / 26 2 - 1 2 ±0. 01 3 ±0. 03 Modelling of signal processes p T µ ( SMT ) [GeV] η µ ( SMT ) Monte Carlo event generator 𝑏, 𝑐-hadron production fractions (a) 0.
Therefore, the magnetic behavior of this complex can be attributed to the two spin sequences comprising eight S = 1/2 centers in the exchange interaction, i. e. , [Cu1-Rad1-Cu2-Rad2-Cu3-Rad3-Cu4-Rad4] (Scheme 3). To describe the behavior, three exchange interactions must be considered, namely, J1, between the Cu II ions and the NO Molecules 2023, 28, x FOR PEER REVIEW 6 of 13 groups in the equatorial plane; J2, between the Cu II ion and the axially coordinated NO group; and J3, accounting for the interaction between Cu II ions and the NO group through the pyridine ring. Scheme 3. Magnetic coupling pathways in complex 1. PHI software was employed to simultaneously analyze the χMT vs. T and M vs. H behaviors  [54]. The best fit to the experimental data gave J1 = -350 (1) cm -1 , J2 = 25. 0 (3) cm -1 , J3 = 2.
Molecules 2023, 28, x FOR PEER REVIEW 6 of 13 groups in the equatorial plane; J2, between the Cu II ion and the axially coordinated NO group; and J3, accounting for the interaction between Cu II ions and the NO group through the pyridine ring. PHI software was employed to simultaneously analyze the χMT vs. T and M vs. H behaviors  [54]. The best fit to the experimental data gave J1 = -350 (1) cm -1 , J2 = 25. 0 (3) cm -1 , J3 = 2. 10 (3) cm -1 , gCu = 2. 03 (1) and grad = 2. 0 (fixed). The value obtained for J1 confirms a strong and antiferromagnetic Cu-NOeq interaction, which can be attributed to the effective overlap of the magnetic orbital (dx 2 -y 2 ) of the Cu II ion and the magnetic π* orbital of the radical [1]. The obtained value (-350 cm -1 ) is comparable to those reported for the equatorial coordination of NIT to Cu II  [55] [56] [57] [58].
The positive value for J2 confirms the anticipated ferromagnetic Cu-NOaxial interactions resulting from the orthogonality of the magnetic orbitals (dx 2 -y 2 ) of Cu II and π* of the radical  [59]. The found strength for J2 is consistent with the Cu-NO exchange interactions reported for similar Cu-NIT complexes  [6, 60, 61]. The small J3 accounts for the ferromagnetic interaction due to spin polarization between Scheme 3. Magnetic coupling pathways in complex 1. PHI software was employed to simultaneously analyze the χ M T vs. T and M vs. H behaviors  [54]. The best fit to the experimental data gave J 1 = -350 (1) cm -1 , J 2 = 25. 0 (3) cm -1 , J 3 = 2. 10 (3) cm -1 , g Cu = 2. 03 (1) and g rad = 2. 0 (fixed). The value obtained for J 1 confirms a strong and antiferromagnetic Cu-NO eq interaction, which can be attributed to the effective overlap of the magnetic orbital (d x 2 -y 2 ) of the Cu II ion and the magnetic π* orbital of the radical [1].
Materials and Physical Measurements All solvents and chemicals used in the synthesis were of analytical grade. The bi-NIT-3Py-5-Ph biradical ligand was synthesized following literature methods  [72, 73] , and the specific synthesis process of the bi-NIT-3Py-5-Ph radical ligand is shown in Scheme S1. Elemental analysis was performed on a PerkinElmer 240 elemental analyzer. FT-IR data were obtained by using a Bruker-Vector 22 Spectrophotometer. Magnetic measurements were performed on a SQUID MPMS XL-5 and VSM magnetometer, in which samples containing Dy and Tb ions were mixed with grease to avoid orientation effects. The data were corrected for the diamagnetic contributions of the sample holder and for all of the constituent atoms using Pascal's table  [74]. 3. 2. Synthesis of the Complexes 3.
Detection of downy mildew spores quantified by qPCR only occurred by the end of June, illustrating that the passive sampling method was not sensitive enough to catch the beginning of the infection. Indeed, the spore detector is equipped with a fan that actively captures the particles at a fixed flow rate, rather than rely on the sedimentation of these within the sample holder. This also accounts for the large difference in maximum number of spore values. Furthermore, some holes in the qPCR data can be seen and are attributed to either an extraction issue or a damage of the scotch tape. To improve the sampling technique and increase its sensitivity, the SporeStick rotating-arm samplers (OptiSense) with matches coated in petroleum jelly were installed in the field during 2022. However, the season was very dry with no downy mildew and only some powdery mildew at the end of the season.
However, advancements in machine learning and neural networks can enable the automatization of the analysis whilst also distinguishing between these very similar particles In this study we used the bag of features technique which can extract and represents any relevant or recurring features within a given image. The database consisted initially only of the classes of interest, namely PV and EN spores but individual classes for the main interferants were subsequently added to improve the distinction possibility. The total database amounts to over 30 000 images spread over the 8 classes, 80% of which was used to train the AI network and the remaining 20% for testing. Figure  3  shows the preliminary results of this algorithm, depicted as a confusion matrix, with a precision of 86. 1%. It successfully distinguishes many of the pollens and spores from one another apart from a slight hesitation between Castanea Sativa pollens and PV as well as EN and Quercus Robur pollens, which could be the consequence of slightly less abundant ground truth databases.
Conclusion Significant advancements have been made to tackle the issues regarding the sensitivity to various species of spores by introducing a laser-based, digital, holographic technique integrated into the existing stations. This technology was used to successfully determine the first infection confirmed by the observation of symptoms after the incubation period and, though the validation using a multiplex qPCR is still ongoing, the preliminary results are very promising. Additionally, planned field trials where successive decisions to treat or not according to the image analysis outputs will be evaluated, which in fine will enable the determination of quantitative spore thresholds to trigger the application of fungicides. Furthermore, the autonomous device allows for both highly resolved spatial and temporal data providing farmers with precise information regarding the situation in their fields when the spores are airborne and have not yet infected the crops.
Urgent action is needed to ensure humanity's future under climate change. Agriculture faces major challenges as it is both influenced by and contributes to climate change. Conservation agriculture sequesters carbon (C) in the soil due to practices such as reduced tillage and planting of cover crops. This study assessed effects of an innovative conservation agriculture popcorn (Zea mays) and wheat (Triticum aestivum) crop rotation in south-western France on soil C sequestration, GHG emissions and several environmental impacts. Two complementary approaches were used: i) a comparison based on field data and expert judgement to assess short-term effects and ii) modelling of three scenarios to quantify longterm outcomes. In both approaches Life cycle assessment (LCA) was used to compare popcorn and wheat rotations.
Comparison to assess short-term effects. For the conservation agriculture crop rotation field data (i. e. farmer practices and yields) was collected on the Nataïs commercial farm (43˚52' North, 0˚89' East) in the Gers administrative department (south-western France) during two rotations: 2017-2019 and 2018-2020 (Table  1 ). Conservation agriculture results for each crop were calculated by averaging results for five wheat or popcorn fields. With slopes of 4-12%, the Nataïs farm's soils are prone to erosion, even though they contain 25-30% clay. The study site has a temperate oceanic climate according to the Ko ¨ppen-Geiger climate classification  [20] , with mean monthly precipitation of 54 mm and mean annual air temperature of 15. 1˚C from 2018-2020. The 2018-2020 period was relatively warm, and the 30-year mean air temperature was 13.
Life cycle inventory. Life cycle inventories (LCIs) were calculated using the MEAN-S-InOut web application v3. 0, which is a customized agricultural LCA tool that generates LCIs of agricultural production systems  [21]. It contains forms to guide data entry, a reference data set for the pollutant emissions and resource use of main inputs of agri-food systems, analytical models to estimate direct pollutant emissions and resource use, and an export function that generates LCI files ready to be imported into LCA software (here we used Simapro, see 2. 1. 3) to calculate impact indicators. Databases used for background processes (e. g. production of fertilizer, irrigation infrastructure), were AGRIBALYSE v3. 0. 1 and ecoinvent v3. 5. Emissions to the air (ammonia (NH 3 ), nitrous oxide (N 2 O), N oxides (NO x ), CO 2 ), water (NO 3 , phosphorus (P), phosphate (PO 4 ), cadmium (Cd), chromium (Cr), copper (Cu), mercury (Hg), nickel (Ni), lead (Pb), zinc (Zn)) and soil (Cd, Cr, Cu, Hg, Ni, Pb, Zn, pesticides) were calculated using models recommended by the AGRIBALYSE methodology  [22].
NH 3 emissions from organic fertiliser application were modelled using EMEP/EEA 2016 Tier 2  [23]. N 2 O emissions were modelled using IPCC 2019 Tier 1  [24]. NO x emissions were modelled using EMEP/EEA 2009 Tier 1  [25]. NO 3 leaching was modelled according to Tailleur et al. [26]. CO 2 emissions from fuel combustion and emissions of active substances of pesticides were modelled according to ecoinvent 1 v2  [27]. Composting of green waste consists of the following processes: shredding, stabilisation, mixing, composting and screening  [28]. The compost of green waste LCI available in the AGRIBALYSE database  [28]  attributes all polluting emissions of the composting process to the compost, and thus to the crop on which the compost is applied. From a methodological point of view, this is questionable, as the composting process has two functions: waste treatment and compost production.
According to the LCI for compost in the AGRIBALYSE database, producing 1 t of compost requires 3 t of green waste. The cost of treating green waste by composting was assumed to be 60 €/t  [29]  (i. e. 180 € per t of compost), and the price of the green waste compost delivered to the Nataïs farm was 14 €/t. Given a total cost of 1 t of compost of 194 €/t, 7. 2% of it (i. e. 14/194×100) was for the compost and the rest for the waste treatment process. Consequently, following an approach proposed by Christensen et al. [30] , we used economic allocation to allocate pollutant emissions and resource use between the two functions: 7. 2% to compost production and 92. 8% to waste treatment. 2. 1. 3 Impact assessment: LCA impact categories. Impacts were assessed using SimaPro software v9. 0. 0. 35. We calculated climate change, terrestrial acidification, and marine eutrophication impacts according to the ReCiPe 2016 Midpoint (H) characterisation method  [31].
e. mean annual air temperature and water balance). The model runs on a yearly basis at the field level to estimate SOC stock at a depth of 0-30 cm. To estimate the amount of crop biomass left on the field after harvest, crop yield is converted into above-ground biomass using a crop-specific harvest index. Below-ground biomass is estimated using an allometric relationship with aerial biomass  [35]. Total (above-and below-ground) biomass minus crop yield equals the amount of biomass that provides fresh organic C inputs. In the comparison of the conservation and the conventional agriculture crop rotation field data for crop yields were available for conservation agriculture but not for conventional agriculture. We assumed that crop yields of the latter were identical to those of the former. This is not necessarily true, we therefore conducted a sensitivity analysis assessing the effects of a 10% increase or decrease of the yield of conventional crops on carbon storage and subsequent environmental impacts.
Study limitations The amount of dry matter (crop residues and compost) received by the conservation agriculture system was 9. 6 t/ha in 2019, and 16. 8 t/ha in 2020. Application of crop residues and compost increases N 2 O emissions. This was taken into consideration in our estimation of N 2 O emissions. However, N 2 O emissions are particularly increased when residues are left on the soil as a mulch rather than incorporated in the soil due to inversion tillage  [51, 52]. This effect was not taken into account in the modelling of N 2 O emissions according to the IPCC 2019 Tier 1 method we used. Consequently, we may have somewhat underestimated N 2 O emissions of the conservation agriculture system. The assessment of short-term effects of conservation agriculture was based on real-life farm data on yield and farming practices for the conservation rotation, while farming practices of the conventional rotation were based on expert judgement and its crop yields were assumed to be identical to those of the conservation rotation.
Although the assumption regarding yield level is supported by the literature and we consider the expert judgement on farming practices to be of high quality, the lack of field data on the reference conventional system constitutes a limitation of this study. A 10% decrease or increase of assumed conventional crop yields had a modest effect on C sequestration and climate change impact, suggesting that our results are nevertheless robust. Furthermore effects of the conservation and conventional agriculture rotations on soil carbon dynamics are based on modelling results. This also constitutes another limitation of this study. However, an evaluation of the AMG model by comparing it to SOC results from 60 long-term field trials in France revealed that the model accurately simulated the changes in SOC stocks over time  [34].
Acknowledgments We thank the  Nataïs team  for its support, in particular  Anne-Marie Joliet , who answered our many questions during data collection. We thank  Eric Ceschia  of  CESBIO/INRAE  for his reactivity and help, as well as  Sylvain Hypolite  ( Agro d'Oc ) for his expert knowledge. We thank our colleagues of the  Carbon Farming project, especially Gecica Yogo  for her coordination role.
Hayo van der Werf received funding from the  Carbon Farming  research and innovation project (www. climate-kic. org/innovation-spotlight/carbon-farming-is-innovating-land-use/) under grant agreement no. TCL 210065, this funding paid the salary of  Maria Vittoria Guidoboni. The other authors did not receive specific funding for the project. The funders encouraged publication of the cover crop) and 26% of soil C (cover crop + compost). Conservation agriculture resulted in soil C sequestration over several decades, until a new soil C equilibrium was reached. study results, but had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Thereafter, the advent of nextgeneration sequencing techniques and large geo-referenced environmental datasets has dramatically increased the quality and quantity of both genomic resources and environmental factors  [8]. Consequently, the field of landscape genomics has grown rapidly. According to our literature survey (n = 278; Figure  1A ; see the supplemental information online for methodological details), more than 50% of all landscape genomic articles published during the survey period 2007-2021 were issued after 2017. A majority of studies initially focused on plants (especially trees), likely due to their sedentariness. This trend has become more balanced in recent years,.
Environmental data as explanatory variables Researchers have three main data sources to describe the environmental conditions at sampling locations: in situ measurements, remote sensing, or spatial interpolation (Figure  2 ), the latter being clearly the most widely used to date (e. g. , WorldClim;  [15] ). The open access and user-friendly design of large interpolation-based, geo-referenced environmental databases have greatly improved the characterisation and exploration of ecological gradients relevant to identify selective pressures in natural populations. Their convenience is that no additional environmental data has to be collected in the field. The availability of such big data has been facilitated by initiatives led by international consortia to acquire, model, store, and share environmental data on a global scale.
For example, the World Ocean Database  [16] , powered by over 20 000 datasets, centralises a coordinated effort of uniformly curated data for oceanographic, climate, and environmental research. With such resources, ambitious studies have tackled the genomics of adaptation over continental-scale areas (e. g. ,  [17] ; Figure  1C ), leaving sampling effort and the cost of genomic analyses as the main bottleneck. Nevertheless, further efforts in data acquisition and interpolation modelling are still needed to better grasp environmental variation, particularly in poorly sampled areas of freshwater and marine ecosystems. In parallel, highly accurate pocketsized devices have been developed to measure biophysical properties at sampling locations, Genotype-environment association (GEA): also known as environmental association analysis, an approach that seeks associations between environmental and genetic variation based on correlative statistical approaches to identify the molecular basis of local adaptation and the environmental variables that drive it.
This study illustrates the benefit of incorporating diverse environmental data conditioned on hypotheses to assess the multifactorial nature of local adaptation. While soil plays a key role in the establishment and persistence of many organisms, including animals, fungi, and plants, few studies have focused on below-ground environmental properties in GEA analyses because appropriate data were largely missing. Yet, great efforts have been made to generate comprehensive datasets of interpolated soil descriptors at decent spatial resolution to complement in situ measurements. For instance, a global prediction of bacterial diversity has been established to provide the first reference atlas of dominant bacteria on Earth  [23] , based on random forest modelling using interpolated soil data at high spatial resolution (i.
Trends in Ecology & Evolution With numerous geo-referenced environmental databases and knowledge of a species' life history at hand, it is possible to explore and test new adaptation-focused hypotheses by going beyond the often-chosen strategy of using conveniently available factors such as those related to bioclimatology or geography. Although there is probably no single data source that meets all needs in terms of spatial and temporal extent and grain, a set of complementary datasets can be used to capture environmental heterogeneity in space and time (Figure  3 ). For instance, when studying selection based on long-term climatic means versus minimum/maximum values or extreme climate events, the gradual change in average climate conditions (e. g. , sum of annual precipitation for 1970-2010) could be teamed with hourly or daily data (e.
g. , precipitation during the growing season 2018) to assess local and singular pulses that are strong enough to induce rapid evolution within populations. In addition, as the temporal grain size commonly increases going back in time due to the diminishing number of observations, a trade-off has to be made between incorporating environmental conditions in the early life stages of long-lived organisms and the accuracy of the predictors tested in GEA. This appears to be particularly challenging for extreme events such as droughts, although there has recently been substantial improvement combining multiple independent datasets (e. g. ,  [31] ). At what spatial scale selection operates seems an impractical question, as selection acts on phenotypes of single individuals and therefore has no explicit spatial scale.
For instance, the spatial locations of samples were transformed with Moran's eigenvector maps and then used as additional explanatory variables to the environmental predictors in statistical analysis  [39]. However, with the development of large genomic datasets (Figure  1F ), we recommend incorporating neutral genetic structure directly as a covariate in statistical models, for example, using the residuals after linear regression on overall structure (partial RDA  [40] ) or as random factors characterised by a covariance matrix (Bayenv2 and BayPass2  [41, 42] ; Box 2) or latent factor(s) (LEA3  [43] ; Box 2). These covariates are best assessed with a set of independent neutral loci or with the full genomic dataset available that presumably captures the overall population structure, reducing false-positives under various demographic scenarios  [11].
Variable selection uses backward stepwise selection or forward stepwise selection of environmental factors to retain the most informative and largely independent predictors in a model, thus avoiding model overfitting. The choice of the best model, and thus of the variables retained, is usually based on Akaike's information criterion, the Bayesian information criterion, or adjusted R 2. However, stepwise regression procedures in landscape genomics suffer from omitting cross-validation using an independent dataset to assess possible biases in parameter estimation  [47]  when moving from explanatory to predictive applications (but see  [48] ).