dataesr/scientific-paragraphs-categorization huggingface dataset:
Accepted Article This article is protected by copyright. All rights reserved. the square of the distance from the instrument. To take this into account, we choose to filter out the data according to the value of the Signal to Noise Ratio (SNR), which guarantees that a clearly visible signal is kept. Thus a minimal value of SNR is considered in order to remove precipitation measurements that are strongly impacted by noise before any comparisons. The choice of this threshold as well as its impact on the observations are detailed in S. 7. In the model comparison (not in models statistics), we choose a threshold of 0. 28mm per day. This criterion has been established by  Palerme et al. [2014]  to optimize the agreement between observations (CloudSat in  Palerme et al. [2014] , MRR here) and the numerical simulations, based on a comparison between ERA-Interim and CloudSat datasets.
| Evaluation of the detection of snowfall occurence A contingency table is used to assess the quality of the simulations and reanalyses results about daily occurence of snowfall regardless of quantity, except the minimal threshold of 0. 28mm per day for daily snowfall that is chosen to distinguish days with precipitation from those without (or negligible) precipitation in the measurements or numerical outputs. A dichotomous result based on the threshold mentioned above is attributed for each day and at each level (for the comparison with the MRR dataset -considering the closest model level to the level of the MRR). In the special case of the inter-comparison between observations (see Figure  S. 3), the minimum threshold retained to consider precipitating days is 0mm. day -1. An additional comparison has been made between visual and instrumental observations of surface snowfall occurences (see S5) showing a good agreement between human observations and data from the snow gauge.
The authors would like to dedicate this article to Chantal Claud, who unfortunately passed away while this work was being carried out, in gratitude for her support and her experienced advice. This paper is a contribution to the CALVA project supported by the French Polar Institute (IPEV project 1013) and to the YOPP, a flagship activity of the PPP, initiated by the WWRP of the WMO. This research was carried out in the framework and with support of the French space agency CNES/CNRS GDR EECLAT. CDA thanks FCT/MCTES for the financial support to CESAM (UIDP/50017/2020 and UIDB/50017/2020) through national funds and support to project ATLACE (CIRCNA/CAC/0273/2019) funded by FEDER, through COMPETE2020-Programa Operacional Competitividade e Internacionalização (POCI). We thank.
acknowledgements The authors would like to dedicate this article to Chantal Claud, who unfortunately passed away while this work was being carried out, in gratitude for her support and her experienced advice. This paper is a contribution to the CALVA project supported by the French Polar Institute (IPEV project 1013) and to the YOPP, a flagship activity of the PPP, initiated by the WWRP of the WMO. This research was carried out in the framework and with support of the French space agency CNES/CNRS GDR EECLAT. CDA thanks FCT/MCTES for the financial support to CESAM (UIDP/50017/2020 and UIDB/50017/2020) through national funds and support to project ATLACE (CIRCNA/CAC/0273/2019) funded by FEDER, through COMPETE2020-Programa Operacional Competitividade e Internacionalização (POCI). We thank.
Here, we ask whether well-established coordination principles that apply to the leaf and plant scales are conserved at the ecosystem scale. Based on an extensive dataset from 98 global eddy-covariance flux measurement sites, and vegetation data collected in-situ and from global databases of plant traits, we explore ecosystem-scale analogues to the relationships between functional traits identified by i) the leaf economics spectrum  5, 8  , ii) the global spectrum of plant form and function 1 , and iii) the least-cost hypothesis  7, 10.
Leaf economics spectrum at the ecosystem scale To analyze if the leaf economics spectrum 8 is propagated to the ecosystem scale, we conducted a principal component analysis based on five ecosystem functional properties and vegetation properties analogous (i. e. representing the same or similar process) to the leaf scale. We used the site-weighted means of nitrogen per mass (wNmass), leaf longevity (wLL), and leaf mass per area (wLMA), the photosynthetic capacity of the whole ecosystem (GPPsat), and the maximum ecosystem respiration (RECOmax, Table  S1 ). The ecosystemscale economics spectrum was apparent from the results of the principal component analysis, where two retained principal components (PC) cumulatively explained 82. 3 ± 4. 7 % of the variance in the dataset (Figure  1 ). In particular, the first principal component showed strong negative loadings of the site-weighted means of leaf mass per area and leaf longevity (wLMA: -0.
These three components cumulatively explained 82. 7 ± 4. 3 % of the variance in the dataset. The first component reflected properties related to the maximum rates of ecosystem processes. Photosynthetic capacity was the main variable contributing positively to PC1, with further strong contributions and positive loadings from all other variables with the exception of wSSD and wLMA (Figure  2c  and Figure  2d ). The only strong negative loading on the first component was the leaf mass per area (wLMA , -0. 61 ± 0. 11). This reflects the performance-persistence trade-off expected by the leaf economics spectrum, and showed that this coordination principle dominated the variability among ecosystem functional properties. The second principal component was primarily defined by variables connected to structure and/or foliar chemistry: nitrogen content and stem specific density loaded negatively (wNmass: -0.
Conversely, other trade-offs might be more elusive due to a set of potential issues underlying the data, due to scale-emergent properties (e. g. structure or evaporation), or due to properties intrinsic to ecosystem-level processes (e. g. optimization of nitrogen use and water use is secondary to the dimension of maximum rates). Therefore, accounting for potential confounding factors such as canopy structure, leaf area index, or processes such as evaporation is important for an accurate representation of ecosystem-level processes and relationships. We acknowledge some potential shortcomings. First, a mismatch between site-level conditions and plant traits from secondary data sources is possible, since plant trait values from databases do not necessarily represent adaptations to the local site conditions (e.
g. LL, LMA, SSD). However, encouraging results indicate that this may not be a major issue. In the case of leaf nitrogen, a recent study showed that it is possible to use the TRY database and maintain robust relationships with ecosystem scale GPPsat  23. Moreover, for European forests it is possible to use traits from the TRY database and obtain very similar community weighted means compared to the in-situ data  24. Second, the different length of flux measurements available at site level impact the calculation of the ecosystem functional properties, particularly in sites with extreme weather conditions and few years of data. We accounted for this shortcoming by selecting the maximum (or potential) value of ecosystem functional properties (e. g. GPPsat, Gsmax) within the measurement period.
At this scale, scale-emergent properties weaken the relationship between the variables connected to the least-cost hypothesis, when not properly accounted for. The relationships underlying the least-cost hypothesis might therefore not always be conserved at the ecosystem scale, which can be explained by multiple reasons. First, some of the previous studies on the least-cost hypothesis generally focused on limited geographical ranges  7, 10, 38. Our dataset displays much stronger variation in plant resource use patterns along axes of nutrient availability and disturbance. However, global scale evidence for the least-cost hypothesis also exists based on modelling  40, 41  , or global measurements of leaf-level carbon isotopes  42. In our analysis, this optimality principle might be more elusive at the global scale, because we simultaneously characterize other trade-offs on the main axisthe component of maximum rateswhich dominates the gradient in average ecosystem functional properties  30, 32.
We excluded sites where the total sum of known species abundances was below 50% of the total site area. For each site, we computed the community mean weighted by species fractional cover  53  for the following plant traits: leaf longevity (wLL, months), leaf dry mass per area (wLMA, mg mm -2 ), stem specific density (wSSD, g cm  -3  , with SSD defined as stem dry mass per stem fresh volume), and nitrogen per leaf mass (wNmass, %). For some sites, site principal investigators provided site-level estimates of plant traits upscaled with similar methodologies, which were prioritized over TRY-derived estimates. We calculated weighted nitrogen per leaf area as the product of wNmass and wLMA (wNarea, g N m leaf -2 ). We collected canopy height (Hc, m) and maximum leaf area index (LAImax, m 2 m -2 ) from FLUXNET or Ameriflux ancillary data products, site principal investigators, and the literature  30, 49.
Eddy Covariance Fluxes and Ecosystem Functional Properties We calculated ecosystem functional properties from carbon, water, energy fluxes, and meteorological data measured or estimated at half-hourly/hourly time steps at the selected FLUXNET sites. Table  S1  provides a comparison between leaf-and plant-level traits and the analogous ecosystem functional properties and vegetation properties used in this study, while Table S8 lists all the variables used in the computations of ecosystem functional properties. We used gross primary production (GPP) and ecosystem respiration (RECO) estimated from measured net ecosystem exchange (NEE) using the night-time partitioning method  54. The methodology for the calculation of each ecosystem functional properties used in this study is described below.
We retained data with good quality (quality check 0measured data, and 1good quality gap-filled data), and, additionally, we retained the data measured during the active growing season, determined as the period when daily GPP is above the 30% of the difference between maximum and minimum daily GPP. For each site, we aggregated the filtered halfhourly/hourly data to mean yearly values for air temperature, vapor pressure deficit, and incoming shortwave radiation (SWin, W m -2 ), and mean yearly cumulative values for precipitation. Transpiration flux estimates were calculated following the methodology in Nelson et al. (2018)   55. We collected elevation information for each site from the FLUXNET or Ameriflux Biological, Ancillary, Disturbance and Metadata (hereafter: ancillary data), FLUXNET websites, and the OzFlux website for one Australian site.
Photosynthetic capacity (GPPsat) We filtered half-hourly/hourly flux data based on SWin to exclude night-time values (SWin > 100 W m -2 ). We fitted GPP and SWin to a hyperbolic light response curve with a moving window of 5 days, with the values assigned as the center of the moving window  30. For each moving window, we extracted the photosynthetic capacity at light saturation (GPPsat, µmol CO2 m -2 s -1 )  23, 52, 56, 57  as the value of the fitted functions at a saturating photosynthetic photon flux density of 2000 μmol m -2 s -1. The photosynthetic photon flux density was derived as SWin * 2. 11  58. We excluded GPPsat estimates above a threshold of 60 µmol CO2 m -2 s -1 to omit unrealistic values of GPPsat according to the distribution of GPPsat. For each year and growing season, we extracted the 95 th percentile from the GPPsat estimates.
Maximum surface conductance (Gsmax) We retained half-hourly/hourly flux data with the same filters described for the calculation of EF, and we additionally excluded noisy measurements with vapor pressure deficit below 0 Pa. We computed the aerodynamic conductance for heat transfer (Ga, m s -1 ), and calculated the surface conductance (Gs, m s -1 ) by inverting the Penman-Monteith equation, using the bigleaf R package  59  and following the methodology in Migliavacca et al. (2021)  30. For each site, we computed the maximum surface conductance (Gsmax, m s -1 ) as the 90 th percentile of Gs values over the available measurement period.
Statistical analysis For each of the three hypotheses examined, we conducted Principal Component Analysis on selected variables, in order to avoid clustering of a priori known strong correlations, reduce the dimensionality of the datasets, and thereby increase the interpretability of the data  61. We used the PCA function in the FactoMineR R package  62. For standardization, we applied ztransformation to each variable. We tested the number of significant principal components to be retained following Dray's method  22  , using the ade4 R package  63, 64  , in order to minimize redundancy as well as loss of information. For each section of the analysis, we obtained subsets of the dataset via substitution bootstrapping using the bootstrap function in the modelr R Package 65 (499 replicates). We repeated the principal component analysis on the bootstrapped datasets and then computed the standard deviation from the bootstrapped outputs to obtain the bootstrap standard error for the explained variance, contributions, and loadings.
Acknowledgment This work used eddy covariance data acquired and shared by the FLUXNET community, including these networks:  AmeriFlux, AfriFlux, AsiaFlux, CarboAfrica, CarboEuropeIP, CarboItaly, CarboMont, ChinaFlux, Fluxnet-Canada, GreenGrass, ICOS, KoFlux, LBA, NECC, OzFlux-TERN, Swiss FluxNet, TCOS-Siberia , and  USCCC. The FLUXNET eddy covariance data processing and harmonization was carried out by the  ICOS Ecosystem Thematic Center ,  AmeriFlux Management Project  and  Fluxdata  project of FLUXNET, with the support of  CDIAC , and the  OzFlux, ChinaFlux  and AsiaFlux offices. We thank  Weber U. ,  Holst J. ,  Meyer W. , Hughes H. , Nave L. , Kosugi Y. , Stuart-Haëntjens E. , Arndt S. , Battles J. , Desai A. , Moore T. , Vogel C. , Munger W. J. , and  York R. for contributing data used in this study.
U. Gomarasca  and  M. Migliavacca  thank the  International Max Planck Research School (IMPRS). This work was also supported by the  Swiss National Science Foundation  ( 40FA40_154245 ;  20FI21_148992 ;  20FI20_173691 ;  20FI20_198227 ) to  N. Buchmann , the  National Research Foundation of Korea (NRF)  grant funded by the  Korea government (MSIT)  ( 2022R1A2C1003504 ) to  C. Byun , the  German Research Foundation DFG  ( INST 186/1118-1 FUGG ) and the  Ministry of Lower-Saxony for Science and Culture (DigitalForst: Niedersächsisches Vorab, ZN 3679)  to  A. Knohl , the  Estonian Research Council  team grant  PRG537  to  Ü. Niinemets , the  Organismo Autónomo de Parques Nacionales  (project  2822/2021 ) to  O. Perez-Priego , the  Spanish Government  grant  PID2019-110521GB-I00  to  J. Peñuelas ; the  U.
Methods Eddy Covariance FLUXNET Sites We used data from the global network of eddy covariance flux tower stations (FLUXNET), integrating the LaThuile dataset  45  with the FLUXNET2015 dataset  46. In case of overlap of sites in the two datasets, the FLUXNET2015 dataset was used. We excluded cropland sites in order to avoid the influence of intense management practices (irrigation, ploughing, fertilization, etc. ). The dataset used for the analysis included sites with more than 3 years of data and availability of ancillary data described below. The selected 98 sites cover different biomes and climate zones: from tropical, Mediterranean, temperate, and boreal to arctic sites, including major forest types, grasslands, savannas, shrublands, and wetlands (Table  S5 , S6).
Plant Traits and Vegetation Properties For each FLUXNET site we collected a set of plant traits for constituent species or site means (leaf longevity, leaf mass per area, nitrogen per leaf area, nitrogen per leaf mass, and stem specific density), and site-level vegetation characteristics (canopy height, maximum leaf area index) from the FLUXNET or Ameriflux ancillary data, or, if not reported, directly from site principal investigators. Where site measurements were unavailable, we included information from the TRY database  47  (a full list of plant traits data sources can be found in Table  S7 ), or data from the literature for the specific sites  30, 48, 49. We obtained site constituent species and species abundances at the sites (percentage of area covered by each species) from the literature  [48] [49] [50] [51] [52]  , and by consulting site principal investigators.
The magnetic field B was deduced from the field mapping data and the actual current measurement applied by the power supply. The position of the centered beam including the scaling of the B-field for a selected charge state was determined with a precision of ±0. 2 mm. When the different targets were remotely inserted on the beam axis, the centering procedure at F3 was the same as was carried out without a target. The ion-optical dispersion from F2 to F3 was employed to take into account possible small deviations from the exact center at the TPC. The calibration of the ion-optical disper-Fig. 1. Scheme of the experimental set up. The accelerated 208 Pb ions, extracted at different energies from the SIS-18 synchrotron, are transported to and focused on a charge-changing foil placed at the entrance F0 of the FRS spectrometer.
The efficiency and linearity of the TPC was measured and taken into account over the full acceptance range. The unambiguous charge-state identification was based on the magnetic field calibration and verified by the observable atomic shell-gap signature in the spectra. Exemplary results of our charge-state measurements are presented in Fig. 2  for lead ions emergent from different C, N 2 , Kr, Zr targets at different energies. The data points were obtained at the different exit energies. In all cases, the targets were thick enough to provide equilibrium charge-state distributions. Possible influences of non-equilibrium were avoided by careful selection of the incident charge states of the projectiles, as explained above. At lower energies the measured mean charge states in solids (C, Zr) are systematically higher than in the compared gases (N 2 , Kr).
The difference is approximately 5-6% at 30 MeV/u and has vanished by 280 MeV/u. The presented data of the gaseous targets were corrected for the influence of the gas-cell windows. This small correction was performed by extended GLOBAL  [27]  and MOCADI  [28]  computer simulations with adjustment of the charge-changing cross sections to the measurements of the separately measured pure window material. A comparison of our measured results with theoretical predictions indicated deviations of up to 12%. We employed for this comparison the semi-empirical Schiwietz-Grande formula  [26]  and the predictions of the computer code GLOBAL  [27]. Another important charge-state simulation code is ETACHA  [29] , which takes into account the temporary population of excited states during the passage of the target.
The number of q data points is determined by both the number of selected accelerator energies and the number of targets with different thicknesses. The measured q data are connected by a function approximation using a sum of two exponentials (dotted lines). The deviations of the experimental values from corresponding theoretical predictions are compared in the lower parts of the gas-solid pairs in a) and b). The experimental errors are illustrated by shadowed bands. The experimental values are compared with an empirical formula from Schiwietz and Grande (S-G)  [26]  and the widely-used computer program GLOBAL (GL)  [27]. Besides the measurements of the mean energy loss E , more parameters must be considered for accurate stopping-power experiments  [31]. From this aspect, it is valuable to remember the basic definition: dE dx = lim x→0 E x.
In Fig. 3 , the solid lines represent the theoretical predictions by the ATIMA program  [34]  using the Lindhard-Sørensen theory  [35]  modified with the input of our measured mean charge states. For the additional correction terms including the Barkas term  [36] , we used also the experimental mean-charge values instead of the atomic number. The overall agreement with the data is better than 3% and confirms our discussion that accurate knowledge of the charge-state population is the key to theoretical improvements of stopping powers for partially-ionized projectiles in this velocity range. The latter statement is confirmed by strong disagreement when bare projectiles are assumed or semi-empirical formulas are applied. Comparisons with widely-used programs and tables (ATIMA  [34] , DPASS Version 2.
00  [37, 38] , CasP Version 5. 2  [39, 40] , Hubert-Tables  [41] , SRIM Version 2013  [42, 43] ) are shown in Fig. 4. The best agreement, as already mentioned above, is exhibited ATIMA the implementation of the measured mean charges of the present experiment, see 4. In case the of GLOBAL  [27]  is implemented in the agreement is more than a factor 2 worse on average. Note that in the energy range of the present experiment, ATIMA applies the Lindhard-Sørensen (LS) theory  [35]  and can be modified optionally with q taken either from the present experiment, from GLOBAL  [27] , or from the formulas of Ref. [26]. The widely-used SRIM program is based on a scaling of proton-stopping powers at the same velocity with an empirical effective charge. SRIM does not include density differences for gases and solids.
The deviations from our results are up to 10%. The results from the Hubert tables are slightly better and show deviations of the order 5-7%. The Hubert Tables use also the parameterization with an effective charge, derived from fits to experimental data in a relatively small velocity region, similar to the SRIM method, but scale the stopping power of alpha particles. Note that the Hubert Tables are only valid for solids. DPASS exhibits better agreement for the light elements, but deviates up to 27% for the heavier targets. The comparison with the CasP calculations shows better agreement with the gases and deviations up to 10% for solids. In general, the widely-used slowing-down theories cannot accurately describe the present data, because the gas-solid difference is not included or the implementation of the charge-state population is inaccurate.
Acknowledgements It is a great pleasure to acknowledge the excellent technical support of the engineers  K. -H. Behr ,  T. Blatz ,  P. Schwarz  and  B. Szczepanczyk , the accelerator experts and the target laboratories at  GSI and Technical University Darmstadt. We would like to thank  Holger Brand  for his extensive support and expertise in developing the LabVIEW interface for our experiment. We appreciate important discussions with  H. Albers  on the impact of stopping power accuracy for nuclear spectrometer experiments. Furthermore, we thank  P. Sigmund  for fruitful discussions concerning this manuscript and valuable collaboration over more than 4 decades. This work was supported by  Justus-Liebig-Universität Gießen  and  GSI Helmholtzzentrum für Schwerionenforschung GmbH, Darmstadt  under the  JLU-GSI strategic Helmholtz  partnership agreement.
This greatly increases the number of taxonomically identified bones at a site, facilitating a larger dataset to investigate links between taphonomic agents, species representation, and broader patterns of hunting and processing behaviour. ZooMS analysis also allows for the calculation of glutamine deamidation values, which are indicative of protein preservation  (van Doorn et al. , 2012). This has been integrated with ZooMS studies at various Pleistocene sites  (Welker et al. , 2017; Sinet-Mathiot et al. , 2019; Brown et al. , 2021b; Ruebens et al. , 2022; Silvestrini et al. , 2022)  and has the potential to provide additional insights into differing patterns of diagenesis, especially when combined with detailed recording of taphonomic attributes such as weathering, abrasion and bone surface modifications.