dataesr/scientific-paragraphs-categorization huggingface dataset:
Cross-evaluation of redundant measurements and creation of merged datasets. Redundant measurements were cross-evaluated using i) weekly discrete samples collected for post-cruise analysis at NOAA GML as calibration reference for CO 2 , CH 4 , and CO, and ii) ARM measurements as calibration reference for O 3. Ambient air mole fractions were adjusted with respect to reference measurements using the slope and intercept of the correlation with initial (non-adjusted) values (Eq. 1). Time-dependent correction factors (slope and intercept) were used to account for drifts in differences between instruments. Correction factors can be found in Tables  4  and 5. The cross-evaluated adjusted mole fractions were then used to generate hourly-averaged merged datasets in order to limit gaps in the time series and facilitate use by the scientific community.
Data Records Table  6  summarizes data records associated with this work, including the repositories where data are stored. Tables  7  and 8  summarize the list of attributes for datasets collected in the Swiss container and for merged datasets, respectively, both archived on PANGAEA (https://www. pangaea. de/). Datasets collected in the CU container and at Met City are archived on the Arctic Data Center (https://arcticdata. io/); the list of attributes can be found in Table  9. Datasets collected in the ARM container are archived on the ARM Data Archive (https:// www. arm. gov/data/); the list of attributes can be found in Tables  10  and 11. Finally, data inferred from discrete whole air sampling and post-cruise analysis at NOAA GML are available on a dedicated webpage (https://gml. noaa.
63 ). The final merged datasets are referenced to the corresponding NOAA calibration scales, i. e. , the WMO-X2019 CO 2 scale 57 , the WMO-X2004A CH 4 standard scale  34  , and the WMO-X2014A CO scale  35. Due to different sampling frequencies (3-hr vs. weekly snapshot samples), a direct cross-evaluation of redundant VOC measurements (propane, i-butane, n-butane) is not possible. Figure  5  shows the comparison of daily averages (n = 36) and highlights the very good agreement between the two datasets (correlation coefficients of 0. 98, 0. 85, and 0. 93 for propane, i-butane, and n-butane, respectively; Spearman correlation test for paired samples). As no redundant measurements are available for DMS, SO 2 , and Hg(0), a similar cross-evaluation is not possible. Top-notch quality-control procedures were, however, used during the expedition (see Methods section) to ensure validity of the measurements.
Usage Notes The standardized *. txt file format permits easy import into all analysis software commonly used in the atmospheric science community. The files are self-explanatory as they contain all metadata and data. The time series archived on PANGAEA (Swiss container and merged datasets) and the Arctic Data Center (CU container and Met City datasets) are designed such that they can be used without further processing. The CO 2 , CH 4 , and CO merged datasets are referenced to the corresponding NOAA calibration scales, i. e. , the WMO-X2019 CO 2 scale  57  , the WMO-X2004A CH 4 standard scale  34  , and the WMO-X2014A CO scale  35. Most datasets contain a pollution flag indicating when local anthropogenic pollution was detected. Merged datasets do not include a Fig. 5  Comparison of redundant VOC measurements.
Comparison of daily-averaged (a) propane, (b) i-butane, and (c) n-butane mole fractions measured in the University of Colorado (CU) container (y-axis) against atmospheric abundances inferred from discrete whole air sampling for post-cruise analysis at NOAA Global Monitoring Laboratory (x-axis). The black line is the bisector. Note that the CU VOC time series were not adjusted for calibration bias as we did not generate a merged VOC dataset. pollution flag because they were created using clean individual time-series. For datasets collected in the CU container and at Met City, and for discrete flask sampling, no measurements were performed when the wind was out of the clean air sector (hence no pollution flag needed). When available, we highly encourage the use of hourly-averaged merged datasets that limit gaps in the time series.
Acknowledgements Data reported in this manuscript were produced as part of the  international Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC ) expedition with the tag MOSAiC20192020, with activities supported by Polarstern expedition AWI_PS122_00. The authors would like to thank  Lynne E. Gratz  for the Tekran 2505 Hg vapor calibration unit used to check the accuracy of the Hg permeation source before the expedition,  Andrea Baccarini  for his contribution and support with the preparation, calibration and operation of the instruments in the Swiss container before and during the expedition,  David Schönenberger  for the help with the SO 2 permeation source at  EMPA , and the teams at the  Paul Scherrer Institute, Switzerland , and  INAR ,  Univ. of Helsinki , for their land-based support during the MOSAiC expedition.
The  EMME-CARE  project received funding from the  European Union 's  Horizon 2020 Research and Innovation Programme  (grant agreement no. 856612 ). M. K. acknowledges the following projects:  ACCC Flagship  funded by the  Academy of Finland  grant number  337549 ,  Academy  professorship funded by the  Academy of Finland  (grant no. 302958 ),  Academy of Finland  projects no. 1325656 ,  311931 ,  316114 ,  332547 , and  325647 , "Quantifying carbon sink, CarbonSink+ and their interaction with air quality"  INAR  project funded by  Jane and Aatos Erkko Foundation , and  European Research Council (ERC)  project  ATM-GTP  contract no. 742206. The analysis of discrete whole air samples was supported by the  NOAA Global Monitoring Laboratory. J. S. holds the Ingvar Kamprad chair for extreme environments research, sponsored by  Ferring Pharmaceuticals.
Code availability The pollution detection algorithm described in Beck et al. 30  to identify and flag periods of primary polluted data in remote atmospheric time series is available at  64  : https://doi. org/10. 5281/zenodo. 5761101. The TERN peak fitting tool implemented in Igor Pro used for the analysis of the GC-MS/FID chromatograms is available at https://sites. google. com/site/terninigor/.
The high perchlorate (ClO 4 -) to chloride (Cl -) ratios observed at the Phoenix landing site, northern polar region of Mars, have been puzzling since detection. However, a lack of understanding of perchlorate-chloride-water systems under cryogenic conditions makes it difficult to assess ClO 4 -/Cl -ratios during deliquescence-related processes. Here we quantitatively evaluate ClO 4 -/Cl -fractionation in deliquescence-induced brines of magnesium-and calcium-perchlorate-chloride salt mixtures under subzero conditions, by measuring solubility data and constructing temperature-dependent thermodynamic models. We find that under specific relative humidity (RH) and temperature (T) conditions, deliquescence of perchlorate-chloride mixtures may form brines with fractionated ClO 4 -/Cl -signatures.
4 ) 2 •6H 2 O-MgCl 2 •12H 2 O or Mg(ClO 4 ) 2 •6H 2 O-MgCl 2 •8H 2 O cases (Fig. At Gale Crater, the Rover Environmental Monitoring Station (REMS) onboard the Curiosity rover recorded RH-T data. We examined the in situ RH-T data of the first four Martian years (Figure  S17 ), which account for all the available data at the time of writing. Since the REMS data record the RH-T conditions at 1. 6 m above the ground, surface brightness temperatures (T surf ) measured by the Ground Temperature Sensor (GTS) were also processed for comparison. We find that the REMS and GTS datasets show no difference in terms of supporting deliquescence at Gale Crater, but the GTS data contain higher uncertainties that would propagate to the derived RH data (Supplementary Figs. 18  and 19 ). Therefore, we use the REMS data to discuss the Gale Crater environments (Fig.
0 to 13. 3 (Fig. 3b ). For MgCl 2 •8H 2 O and MgCl 2 •12H 2 O instead of MgCl 2 •6H 2 O, using the inferred lowest eutectic points for calculation, the formed brines would have ClO 4 -/Cl -molar ratios of 14. 4 and 62. 6, respectively. Our calculations assume complete separation of formed brines and residual chloride salts and thus provide the upper limit for the deliquescence-induced ClO 4 -/Cl -molar ratios. The measured ClO 4 -/Cl -of 6. 13  15  in Phoenix soil can form via either Mg or Ca salt mixtures, but the Mg(ClO 4 ) 2 •6H 2 O-MgCl 2 •6H 2 O system is unlikely to be the parent salt mixture since it cannot produce a ClO 4 -/Cl -ratio higher than 2. 5. For reference, we also analyzed relevant data from McMurdo Dry Valleys (MDV), Antarctica. The MDV is considered an excellent analog site for the northern plains of Mars because of the arid conditions, intense UV solar radiation, diurnal freeze/thaw cycles, sparse snowfall, cryogenic processes, low mean temperatures, and Mars-like surface topography  35.
3. The overall relative uncertainties of the liquid and wet residual compositions in the experiments were within 2%. The details of the experimental methods and results are available in Supplementary Notes 1 and 2, respectively. Thermodynamic modeling methods and results validation. The ISLEC software, i. e. , Institute of Salt Lakes Equilibrium Calculator, was used in this study to model the solubility isotherms and generate the phase diagrams for the ternary Mg(ClO 4 ) 2 + MgCl 2 + H 2 O and Ca(ClO 4 ) 2 + CaCl 2 + H 2 O systems. The theoretical background, basic equations and implementation of the software can be found in a recent work  60. The software adopted a CALPHAD type (i. e. , Computer Coupling of Phase Diagrams and Thermochemistry) thermodynamic framework and a Pitzer-Simonson-Clegg (PSC) excess Gibbs energy model to describe the non-ideality of the aqueous phase.
Unlike the conventional solubility modeling method, the CALPHAD framework allowed the thermodynamic model for the aqueous system to be parameterized using various types of thermodynamic data, particularly the thermal data (e. g. , enthalpy of dilution, enthalpy of solution, and heat capacity of aqueous solution and solids), to enhance the reliability of the temperature-dependent models. The PSC model was an advanced version of the classical Pitzer model updated by the Pitzer's group  61. The ISLEC software used the Gibbs energy minimization (GEM) approach for solving the phase equilibria. The ISLEC software has successfully modeled extensive binary systems, ternary systems, and multicomponent systems down to ~200 K (see Code Statement for accessible link). A detailed description, parameters, and equations implemented by the ISLEC software for the studied ternary systems Mg(ClO 4 ) 2 + MgCl 2 + H 2 O and Ca(ClO 4 ) 2 + CaCl 2 + H 2 O are summarized in Supplementary Note 3.
When a w is of a solution in equilibrium with the salt, the calculated RH is MDRH. The a w can be calculated using equation S16 in the Supplementary Information. The deliquescence surface data in an x-T-RH coordinate system were generated using the C codes (i. e. , islec_mars_mdrh_mg. c and islec_mars_mdrh_ca. c; see Code Statement for accessible link). The x was calculated from the equilibrium molality of perchlorate and chloride in aqueous solution. These codes can be compiled using the GCC compiler on the Linux platform or the Dev-Cpp compiler on the Windows platform. The awk script can be used to format the output data produced by islec_mars_mdrh_ca for further visualization. These output data were then visualized with Gnuplot software for 2D and 3D plots. Detailed instructions can be found in the README file included in the source code package (link provided in Code Statement).
Acknowledgements We are very grateful to  Scott McLennan  for a constructive review, which greatly improved an earlier version of this paper. This work was supported by  B-type Strategic Priority Program of the Chinese Academy of Sciences  (No. XDB41000000 ),  National Natural Science Foundation of China  (No. 41703064 ,  41673072 ,  42173045 ,  41573056 ),  Pre-research project on Civil Aerospace Technologies  (No. D020102 ),  Key Research Program of the Institute of Geology & Geophysics CAS  (No. IGGCAS-201905 ), and  West Light Foundation of CAS. Pierre-Yves Meslin  acknowledges financial support from  Centre National d'Etudes Spatiales (CNES).
Data availability Rover Environmental Monitoring Station (REMS) data are available in the Planetary Data System (PDS) https://atmos. nmsu. edu/PDS/data/mslrem_1001/DATA. Underlying data for the main figures are included as Excel files in the source data. All data used for plotting the main figures and supplementary figures and those presented in Supplementary Tables are available at Science Data Bank  74  , https://doi. org/10. 11922/sciencedb. 00915. The experimental operation, modeling equations, simulation code, visualization tools, and methodology-related discussions are all available as Supplementary Notes in the Supplementary Information.
Code availability The ternary aqueous chemistry codes (C programs for deliquescence surface calculations; awk script for data formatting; gnuplot script for graph plotting) used in this study are available at Science Data Bank, https://doi. org/10. 11922/sciencedb. 00911. The softwares used in this study are accessible at following links provided. The awk: www. gnu. org/ software/gawk; the GCC compiler: gcc. gnu. org; the Dev-Cpp compiler: sourceforge. net/ projects/orwelldevcpp. The Gnuplot: www. gnuplot. info. The ISLEC software and models can be accessed from a PHP based Web interface with a single point calculation mode (www. islec. net/islec-web/islec-web-mg-cl-clo4; www. islec. net/islec-web-ca-cl-clo4). The LMD General Circulation Model is available on request from the LMD team.
Model description We use simulations from the CESM1 model  [Devaraju et al. , 2016; Tharammal et al. , 2019]  to isolate the impact of LCC on climate. CESM1 is a fully coupled climate model consisting of atmosphere, land, ocean and sea-ice components that are linked through a coupler that exchanges state information and uxes between the components. The atmospheric component of CESM1 is the Community Atmosphere Model version 5 (CAM5). The horizontal spatial resolution of CAM5 used here is 1. 9 o in latitude and 2. 5 o in longitude, and the model time step is 30 minutes. Oleson et al. , 2010]. CLM4 uses transient LCC annual datasets, diagnoses the changes in carbon uxes, and adjusts carbon pools for mass conservation. For example, when the vegetation cover decreases, the loss of carbon is distributed among the litter, wood products, and a land cover conversion ux that is released immediately to the atmosphere.
Machine Learning (ML) confers several advantages over traditional statistical modelling of ecological phenomena, for example, ML can handle different data types (continuous, categorical); it makes no assumptions about the training data or prediction residuals; it can flexibly capture highly non-linear relationships; and it can accommodate missing data, both in the response and in the predictors  [13]. In recent years, several studies have used ML to quantify the links between antecedent environmental conditions including hot, dry weather and burnt area in different regions  [14]. For example, Amatulli et al. [15]  used a decision-tree based Random Forest (RF) ML model to estimate burnt areas in the European Mediterranean countries with the Canadian Fire Weather Index (FWI). Ma et al. [16]  also trained a RF model with the MODIS Global Fire Atlas dataset (2010-2016) to analyse the impacts of climate, topography, vegetation, and socioeconomic variables on forest fire occurrence in six geographical regions in China.
MODIS burnt area Fire activity is represented by burnt area (BA) in this study. BA reflects the outcome of a fire season and integrates ignition events as well as fire weather and fuel characteristics that determine fire spread. Daily BA with 500-m resolution was obtained from the Collection 6 Moderate Resolution Imaging Spectrometer (MODIS) Burned Area product (MCD64A1, https://ladsweb. modaps. eosdis. nasa. gov/search/order/2/MCD64A1-6, accessed 20 May 2020)  [1]. We selected data from January 2001 to February 2020. In addition, we used the MODIS Land Cover Type Product (MCD12Q1, https://ladsweb. modaps. eosdis. nasa. gov/search/order/1/MCD12Q1-6) to derive and extract land cover data at 500 m spatial resolution. MCD12Q1 product has been widely used in Australian fire studies  [31, 32]. We chose this product since it has same resolution as the MODIS Burned Area product.
Both PV and NPV represent opposing yet complementary indicators of flammable fuel availability  [37]. The monthly product of Vegetation Fractional Cover with 500 m spatial resolution is derived from the MODIS Nadir BRDF-Adjusted Reflectance product (MCD43A4) collection 6, starting from January 2001 and actively maintained by the Earth Observation Landscapes team, CSIRO (https://eo-data. csiro. au/remotesensing/v310/Australia/monthly/cover/). MODIS fractional cover data have been validated in Australia  [38]  and previous studies have used this product to estimate soil properties  [39]. Given that f_PV and f_NPV had a strong correlation (R 2 = 0. 88), we selected average f_NPV for the current month and the previous month to represent fuel flammability in each month at 0. 25°across the SE Australian forest areas.
Long-term trend in Normalised Difference Vegetation Index (NDVI) has been shown to reflect changes in biomass or fuel load through time and space  [40] [41] [42]. We used monthly average NDVI with a 5 km spatial resolution from Australia Bureau of Meteorology (http://www. bom. gov. au/jsp/awap/ndvi/index. jsp). These NDVI data are computed from signals received from the Advanced Very High Resolution Radiometer (AVHRR) instruments on board the National Oceanic and Atmospheric Administration (NOAA) series of satellites. We used MODIS Land Cover Type Product to extract NDVI for forest vegetation and resampled NDVI data at 0. 25°resolution. To capture biomass availability for each 0. 25°grid cell, we calculated the median of the 60 monthly average AVHRRderived NDVI values that preceded the fire season for that grid cell over the past five years.
Climate drivers There are three major climate drivers influencing Australia's climate: the Southern Annular Mode, the El Niño-Southern Oscillation (ENSO), and the Indian Ocean Dipole (IOD). SAM describes the north-south movement of the westerly wind belt circling Antarctica and is an important indicator of rainfall in southern Australia. We used an observation-based monthly SAM dataset based on Marshall  [44]  (http://www. nerc-bas. ac. uk/icd/gjma/sam. html). The IOD is the difference in sea surface temperatures in the tropical western and eastern Indian Ocean. It has widespread impacts on precipitation in countries surrounding the Indian Ocean, including India, Indonesia, and Australia  [26]. Monthly IOD series for the study period were downloaded from JAMSTEC (http://www. jamstec. go. jp/virtualearth/general/en/index.
SMOGN has previously been applied to other so-called ''imbalanced data" regression problems like ours where the machine learning model was being used to predict extremely rare values of a continuous target variable. A detailed description of the SMOGN technique especially for input parameters can be found in the Python package (https://pypi. org/project/ smogn/). The correlation coefficient between PAB and each individual fire weather index was greatly improved after using SMOGN pre-processing (Table  S1  online).
All covariates used in each RF model were statistically independent of each other (VIF < 10, Table  S3  online). There are two important parameters in the RF model: (1) number of trees in the forest (ntree) and (  2 ) number of random variables in each tree (mtry). In the RF regression model, the value of mtry commonly used is the number of predictor variables divided by three  [59, 60] , which is the default in the R package of ''ran-domForest". Using a trial and error method to determine the value of mtry, we found that the model accuracy did not increase appreciably compared to that with default mtry. Therefore, we used mtry of 3. We used ntree with 1000 following Ließ et al. [61]  and Wang et al. [60]. Same values of mtry and ntree were used for each RF model to avoid any difference in model performance caused by RF parameters.
There were 3635 SMONG data points used for RF model development. We randomly selected 75% of the total dataset for model development, with the remaining 25% used for validation. This calibration and validation procedure was repeated 100 times, applying a sampling with replacement method to obtain 100 random subsamples of the data, each one with its own calibration and validation dataset  [39, 62]. To evaluate the performance of RF for predicting PAB, we used the two performance measures, coefficient of determination (R 2 ) and relative mean error (RME, %)  [63] : R 2 ¼ P n i¼1 ðO i À OÞðP i À PÞ ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi ffi P n i¼1 ðO i À OÞ 2 s ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi ffi P n i¼1 ðP i À PÞ 2 s 0 B B B B @ 1 C C C C A 2 ; ð1Þ RME % ð Þ ¼ P n i¼1 ðO i À P i Þ P n i¼1 O i Â 100; ð2Þ where P i and O i represent the predicted and observed PAB; P and O represent the means for the predicted and observed PAB and n is the number of samples.
Non-linear relationships between PAB and covariates Fig. 3  shows the relative importance of explanatory variables in determining PAB. Our model shows that PAB_lag1 (18%) was the most important variable, followed by FWI90 (15. 3%), NDVI (12. 1%), FWI_mean (10. 8%), Latitude (9. 5%), Longitude (9. 0%), UW_ratio (8. 9%), f_NPV (8. 7%), and F_ratio (7. 7%). Similar results for variable importance were found in the other three types of RF models (Figs. S8-S10 online). Although PAB_lag1 emerged as the most important predictor of PAB, it is challenging to incorporate it as part of future predictive modelling. It should be noted that re-running the RF model with all environmental features except PAB_lag1 could still explain around 76% of the variation in burnt area (data not shown). A non-linear relationship was detected between PAB of the concurrent month and PAB of the prior month (Fig.
Acknowledgments This work was supported by the  National Natural Science Foundation of China  ( 42088101  and  42030605 ). We are grateful to the  ECMWF (European Centre for Medium-Range Weather Forecasts)  for providing freely available historical fire weather indices via the Copernicus Data server as part of the  Copernicus Emergency Management Service. Allan Spessa  and  Tadas Nikonovas  appreciate support from the research project: Towards an  Operational Fire Early Warning System for Indonesia (TOFEWSI). The  TOFEWSI  project was funded from October  2017-October 2021  through the  UK's National Environment Research Council/Newton Fund  on behalf of the  UK Research & Innovation  ( NE/P014801/1 ) (UK Principal Investigator: Allan Spessa) (https://tofewsi. github. io/). Xiaowei Guo  receives financial support from the  Natural Science Foundation of Qinghai  ( 2021-HZ-811 ).
Therefore, the most pressing question is whether functional similarity decays typically faster or slower along environmental gradients than expected given the taxonomic decay, as suggested by some earlier studies  (Carvalho et al. , 2020; Sokol et al. , 2011; Swenson et al. , 2011). decay was fastest in the datasets from mid-latitudes. Overall, datasets covering larger spatial extents showed a lower rate of decay along spatial distance but a higher rate of decay along environmental distance. Marine ecosystems had the slowest rate of decay along environmental distances.