dataesr/scientific-paragraphs-categorization huggingface dataset:
The δ 15 N of Fpri may reflect seasonally-varied main source regions to Greenland or a dominate 725 role of high-latitude nitrate transport to Summit. At Dome C, both the magnitude of Fpri and its δ 15 N/Δ 17 O indicate a dominant role of stratospheric denitrification on nitrate budget at Dome C. The inverse model is designed to help interpret ice core nitrate records. Applying the inverse model to ice core nitrate records needs knowledge of initial conditions. In 730 particular, archived snow nitrate concentration and its δ 15 N and Δ 17 O, the snow accumulation rate and light absorption impurity concentrations should be known for a https://doi. org/10. 5194/egusphere-2023-1054 Preprint. Discussion started: 14 June 2023 c Author(s) 2023. CC BY 4. 0 License. IDEX-02 (project IDEX Université Grenoble Alpes) and Labex OSUG@2020 (Investissements d'avenir -ANR10 LABX56), the support of the overwintering staffs and the French polar institute IPEV through the SUNNITEDC (1011) and CAPOXI programs (1177) (J.
Calibration was performed by the successive measurements of the reference glass materials NIST SRM 610, 612 and 614 at the beginning, the middle and the end of each analytical session using 43 Ca as internal standard. Data reduction was performed using an inlab developed software FOCAL 2. 41. Accuracy of the measurements was checked by measuring the otolith certified reference materials (CRM) FEBS-1 and NIES-22 against the preferred values from the GeoRem database  (Jochum et al. , 2005) , when available.
e. , disjoint divisions) of the dataset for cross-validation: each fold was successively used as validation fold to assess the model based on the remaining four folds, and a mean classification rate was calculated from the five validation folds. Although linear discriminant analysis has been widely used as the preferred classification method for assessing connectivity from elemental fingerprints in coastal environments  (Becker et al. , 2005; Gomes et al. , 2016) , alternative, more accurate and successful methods have been proposed  (Mercier et al. , 2011; Dixon and Brereton, 2009). We here applied 31 classification methods (available in ClassificationLearner) on the chemistry of encapsulated larval shells in order to identify the best-fitting model (obtained from one classification method and a specific list of predictors) to determine their geographical origin.
For each dataset (all sites, sites in Manus and Woodlark, sites in East region and Woodlark, and areas in East region and Woodlark), we successively performed all models with different numbers and combinations of elements. Model significance was assessed by comparing its classification success rate with that of randomized data (1000 runs) using a modified version of the Matlab code (Supplementary Information S5) by  (White and Ruttenberg, 2007) , which was initially restricted to the discriminant analysis. Shinkailepas tollmanni egg capsules are in contact with the mixture between seawater and hydrothermal fluid associated with the habitat of Ifremeria nautilei, in proportions that vary depending on the vent activity, local currents and turbulence  (Podowski et al. , 2010). This mixture will hereafter be referred to as the 'habitat water'.
, 2022; Plouviez et al. , 2019; Breusing et al. , 2021) , although Woodlark could act as a 'stepping stone' allowing limited connectivity  (Boulart et al. , 2022; Poitrimol et al. , 2022). For S. tollmanni, although no population differentiation has been observed between these regions based on the mitochondrial genome  (Poitrimol et al. , 2022; Yahagi et al. , 2020) , genome-wide data obtained from RADsequencing recently identified a strong genetic break indicating a lack of dispersal between Manus and the eastern region  (Tran Lu Y, 2022, pp. 91). The Woodlark ridge was however a recipient for migrants from the two regions, representing a tension zone  (Tran Lu Y, 2022, pp. 87-89). Based on this information, we split our data in two datasets, comprising sites of the western region (Manus/Woodlark) on the one hand, and eastern region plus Woodlark on the other hand (considering that a larva settling in the eastern region could originate from Woodlark but not from Manus).
Within the western region, the overall accuracy reached 86. 5%, using Mn and Ba as predictors (Fig. 5a ), compared to 26. 1% by random assignment (p<0. 001). Site accuracy ranged from 54. 5% for North Su to 100% for Solwara 8. Similarly, the best fitting model for the eastern sites (including La Scala in the Woodlark Basin) showed an overall accuracy of 77. 1% with Mg, Mn, Fe, Zn, Sr, Ba and Pb as predictors (Fig. 5b ), compared to 23. 7% by chance (p<0. 001). Another model using the same dataset, method and predictors, but focusing on areas instead of sites (Fig. 5c ), obtained a slightly lower accuracy (75. 3% vs 35. 3% by chance; p<0. 001). The site model presents better accuracy due to the strong difference in Mn abundance between the sites from Futuna (Kulo Lasi and Fatu Kapa) that are considered as one single class by the area model.
In most cases, the Support Vector Machines (SVM) method appeared to generate the best classification model (Supplementary Information S4). (Dixon and Brereton, 2009 )) explored different model types from various datasets and concluded that SVM generally performs better than other methods when data normality is not met, which is the case here. Linear discriminant analysis, widely used in the literature, was selected as the most appropriate method for only one of the seven models presented in this work, with a well-defined clustering dataset (Fig. 6a ; see Supplementary Information S4). We aimed to check whether there is, indeed, a site-specific fingerprint. Our dataset of larval shells therefore included at least two different sampling replicates from Ifremeria communities from one site for more than half of the sampled locations.
Acknowledgements This research was funded and carried out under the  ANR   CERBERUS  project ( ANR-17-CE02-0003 ). A CC-BY public copyright license has been applied by the authors to the present document and will be applied to all subsequent versions up to the Author Accepted Manuscript arising from this submission, in accordance with the grant's open access conditions. Preprints are available at https://www. biorxiv. org/content/10. 1101/2023. 01. 03. 522618v1. We would like to thank the captains and crews of the  French Research Vessel L'Atalante  and the team in charge of the ROV Victor 6000 during the two parts of the Chubacarc cruise (Hourdez and Jollivet, 2019; https://doi. org/10. 17600/18001111), without whom sampling would not have been possible. We also wish to thank  Nicolas Gayet  for his help onboard with the PIF, and  Camille Poitrimol  for her help with collecting I.
The electrospray ionization interface (ESI) was operated in positive mode using the following settings: curtain gas 30 psi, ion spray 5000 V, temperature 300 • C, gas 1 and 2 set at 30 and 40 psi respectively, and entrance potential 10 V. Two transitions per toxin were monitored (Table  1 ) with a dwell time of 40 ms for each. Quantification was carried out using a 6-point external calibration curve (from 0. 6 to 145 ng/mL) prepared with the 10 standards (see Supplementary Fig. S1  for an example of chromatogram). Data acquisition and processing were performed using Analyst 1. 6. 3 (Sciex).
Acknowledgments This project has received funding from the  European Union 's  Horizon 2020 research and innovation programme  under the  Marie Sk lodowska-Curie  grant agreement No  899546. MD gratefully acknowledge the support of the  Spanish Minstry of Science and Innovation  through the project  HydroPore  ( PID2019-106887GB-C31 ). The simulation data displayed in the figures can be downloaded at http://hdl. handle. net/10261/331188. Acknowledgments This project has received funding from the  European Union 's  Horizon 2020 research and innovation programme  under the  Marie Sk lodowska-Curie  grant agreement No  899546. MD gratefully acknowledge the support of the  Spanish Minstry of Science and Innovation  through the project  HydroPore  ( PID2019-106887GB-C31 ). The simulation data displayed in the figures can be downloaded at http://hdl.
Acknowledgements This work, presented at ICAWA 2016 conference, was supported by the  Government of the Islamic Republic of Mauritania , through the  World Bank , has received funds from the  International Development Association (IDA)  and the  Global Environment Facility (GEF)  for the implementation of the  Project Regional Fisheries Authority in West Africa "(PRAO-MR ) in Mauritania. Final edition was made within the  AWA  project supported by  French National Research Institute for Sustainable Development (IRD)  and  Federal Ministry of Education and Research (BMBF) at the Sub Regional Fisheries Commission SRFC  (grant  01DG12073E ) and  EU Preface  under grant agreement no  603521.
AOD and ERF changes throughout the historical period The differences in pre-industrial to present-day ambient AOD at 550 nm (Fig. 8 ) have an increasing trend since the 1900s on global scale, but with a much smaller rate since the 1990s (Fig. 8a ). Sulphate AOD has undergone the largest increase since the pre-industrial era, followed by organic aerosol AOD on a global scale and over all the five ATLAS regions. Changes in AOD over ENA, WCE, and MED reached their peak around the late 1970s -early 1980s, with declining trends afterwards (Fig. 8b-d ). On the other hand, AOD changes over EAS and SAS have been following an upward trend since the 1950s (Fig. 8e, f ). Although trends from CMIP6 models after around 2010 are more difficult to assess (as historical simulations end at 2014), the decrease in anthropogenic SO2 emissions over EAS since 2011 was underestimated in the CMIP6 emissions database available at the time of the CMIP6 aerosol simulations  (Hoesly et al.
Financial support. AK, AKG, DA and PZ acknowledge support for enhancing the operation of the National Network for 630 Climate Change (CLIMPACT), National Development Program, General Secretariat of Research and Innovation (2023ΝΑ11900001 -Ν. 5201588). NO was supported by the Environment Research and Technology Development Fund (JPMEERF20202003 and JPMEERF20232001) of the Environmental Restoration and Conservation Agency provided by Ministry of the Environment of Japan, the Arctic Challenge for Sustainability II (ArCS II), Program Grant Number JPMXD1420318865, and a grant for the Global Environmental Research Coordination System from Ministry of the 635 Environment, Japan (MLIT2253). TvN and PLS acknowledge funding from the European Union's Horizon 2020 research and innovation program under grant agreement No 821205 (FORCeS).
The Portuguese perspective The Portuguese perspective section was based on an online literature search in PubMed for studies published up to the 5 th of August 2022 using the search terms "indoor air" or "IAQ" and "respiratory" or "asthma*" or "COPD" or "pulmon*" and "Portugal" or "Portuguese". A total of 77 articles were returned, one being excluded as a duplicate, eight for only being methodological or proceedings articles with no data output, two for being focused on COVID-19, three for not including any IAQ parameter, 27 for not measuring a respiratory outcome, 14 for not including a Portuguese population, two for considering only occupational exposures and two narrative reviews. One extra article meeting the inclusion criteria was identified through cross-referencing. ] [27] [28] [29] [30] [31] [32] [33] [34] [35] [36] [37] [38] [39] [40] [41] [42] [43]  For the quantitative analysis, 13 of the 19 reviewed articles that reported the effect estimates for exposure to indoor air pollutants on the development or exacerbation of respiratory diseases were included.
General update Five systematic literature reviews and meta-analyses (MA) that fulfilled the selection criteria of robustness allowed us to re-examine existing research data about the association between indoor air pollutants and respiratory health. These varied according to the type of respiratory disease and air pollutant. The total number of included studies and respiratory patients in these MA is relevant. Furthermore, two systematic reviews reported on modifiers of air quality. Robust and reliable effects (per 10 mg/m3) from a systematic review and MA including 101 studies were observed for SO 2 with chronic obstructive pulmonary diseases (COPD) (pooled relative risks [RRs] 1. 016, (95% confidence interval [CI] 1. 012-1. 021] and for NO 2 with childhood asthma, preterm birth (a risk factor for asthma), lung cancer, and COPD (RRs 1.
The project was distinguished for merging a complete evaluation of indoor air parameters in 71 classrooms from 20 primary schools with a Fig. 1  Forest plot representing the pooled effects for the association between indoor air pollutants and asthma, resultant from the meta-analysis of the selected studies in Portugal. comprehensive clinical evaluation of 858 children attending those classrooms, including skin-prick tests, spirometry with bronchodilation, exhaled nitric oxide (eNO), pupillometry, exhaled breath condensate (EBC) collection and anthropometric measurements. The children's legal guardians were also asked to fill out an ISAAC-based self-reported questionnaire. By analysing data from the ARIA project, significant associations were identified between high fungal diversity in classrooms and a lower risk of allergic sensitisation in children (0.
Although they contain data from similar GOBMs and pCO2 products, the compiled database of RECCAP2 goes well beyond that used in the framework of the Global Carbon Budget  (Friedlingstein et al. , 2022). Specifically, the RECCAP2 database contains simulation results from a broader set of numerical simulations, and it includes much more spatially and temporally refined data and many more variables. This database permits us to analyze the spatially and temporally resolved air-sea CO2 fluxes and the processes controlling the ocean carbon sink. With this study here, we provide an evaluation of the GOBM hindcast simulations to better contextualize the model results in the different studies of the AGU special issue "REgional Carbon Cycle Assessment and Processes -2 (RECCAP2)" and in the 2020 and 2022 edition of the Global Carbon Budget  (Friedlingstein et al.
RECCAP2 simulations and their relation to CO2 flux components The RECCAP2 database provides model output from 1980 to 2018 from four simulations (called simulations A, B, C and D) that aim to quantify the different components of the oceanic CO2 flux. The four simulations all start in preindustrial times and extend through 2018, however, the GOBMs used different definitions of "preindustrial" with simulations starting between 1765 and 1870, and the corresponding assumed pre-industrial CO2 mixing ratios varying between 278 ppm and 286 ppm. Simulations A and C were forced with historically increasing CO2, whereas simulations B and D were forced with constant pre-industrial CO2. Furthermore, all four simulations were forced with a repeated (normal year) atmospheric forcing until historical atmospheric reanalysis fields became available in 1948 or 1958 (depending on the atmospheric reanalysis that was used to force the GOBM).
GOBMs To compare the total sea-air CO2 fluxes from the GOBMs with observation-based estimates, we utilize the RECCAP2 dataset of pCO2 products, including AOML_EXTRAT, CMEMS-LSCE-FFNN, CSIR-ML6, JenaMLS, JMA-MLR, MPI-SOMFFN, OceanSODA-ETHZ, UOEX_Wat20, and NIES-MLR3 (see table in DeVries et al. (in review)) for references and further details). These pCO2 products are a product of statistical models and sparse observations of surface ocean partial pressure of CO2. We calculate long-term averages and trends over these products for the period 1985 through 2018 only, i. e. , for the period when all products provide estimates. The simulated regional Fnat ss were compared to ocean inversion-based estimates  (Mikaloff Fletcher et al. , 2007). These rely on observations of interior ocean DIC, alkalinity, and nutrients to create a conservative DIC tracer where the anthropogenic concentration in each grid cell is subtracted following  Gruber et al.
(1996)  as well as changes in the ocean interior DIC due to biological processes. In a second step, 10 ocean circulation models were used to determine the circulation pattern by injecting a dye tracer at the ocean surface at a constant rate. Finally, the circulation pattern which results in the best fit with the observations of the adjusted DIC tracer is utilized to determine Fnat ss. To constrain the simulated Fant ss , we used the mapped anthropogenic carbon storage between the years 1800 and 2002 from the GLODAPv2. 2016b-product  (Lauvset et al. , 2016). This data-product is based on the TTD-method  (Matear et al. , 2003; Waugh et al. , 2006)  and henceforth referred to as TTD-estimate. It includes estimates of a mapping error, but a comprehensive error estimate containing observational, methodological, and mapping errors is not provided with the dataset.
, 2022). The ocean part of RECCAP2 assesses the most up-to-date air-sea carbon flux estimates based on statistical methods applied to observations of surface ocean partial pressure of CO2 (pCO2 products) and hindcast simulations from global and regional ocean biogeochemistry models (GOBMs) to better understand the global and regional ocean carbon sink over the last three decades, its decadal and inter-annual variability and seasonal cycle, and the contribution of the biological pump. Although they contain data from similar GOBMs and pCO2 products, the compiled database of RECCAP2 goes well beyond that used in the framework of the Global Carbon Budget  (Friedlingstein et al. , 2022). Specifically, the RECCAP2 database contains simulation results from a broader set of numerical simulations, and it includes much more spatially and temporally refined data and many more variables.
This database permits us to analyze the spatially and temporally resolved air-sea CO2 fluxes and the processes controlling the ocean carbon sink. With this study here, we provide an evaluation of the GOBM hindcast simulations to better contextualize the model results in the different studies of the AGU special issue "REgional Carbon Cycle Assessment and Processes -2 (RECCAP2)" and in the 2020 and 2022 edition of the Global Carbon Budget  (Friedlingstein et al. , 2020 (Friedlingstein et al. , , 2022) )  and to make recommendations for future assessments of the ocean carbon sink using GOBMs. The RECCAP2 project is a continuation of the large efforts that have been undertaken in the last decades to quantify the past and present ocean carbon sink with pCO2 products  (Chau et al. , 2022; Gregor et al.
Acknowledgments The authors have no conflicts of interest. J. Terhaar  acknowledges funding from the  Woods Hole Oceanographic Institution Postdoctoral Scholar Program , and the  Swiss National Science Foundation  under grant # 200020_200511. N. Goris acknowledges funding from the  Norwegian Research Council  through the project  COLUMBIA  (Grant  275268 ) and thanks  Jean Negrel  for his assistance with the preparation of RECCAP2-model and data products for her analysis. J. Terhaar ,  J. D. Müller , and  N. Gruber  acknowledge funding from the  European Union 's  Horizon 2020 research and innovation program  under grant agreement no. 821003  (project 4C,  Climate-Carbon Interactions in the Current Century ). F. F. Pérez  was supported by the  BOCATS2  ( PID2019-104279GB-C21 ) project funded by  MCIN/AEI/10.
13039/501100011033  and contributed to WATER:iOS CSIC PTI. N. Gruber  acknowledges further support from the  European Union 's  Horizon 2020 research and innovation programme  under grant agreement no. 821001  (SO -CHIC ). T. DeVries  acknowledges support from  NSF  grant  OCE-1948955. J. Hauck  and  R. Seferian  were supported by the  European Union 's  Horizon 2020 research and innovation programme  under grant agreement No  820989  (project  COMFORT ). J. Hauck  acknowledges funding from the  Initiative and Networking Fund of the Helmholtz Association (Helmholtz Young Investigator   Group Marine Carbon and Ecosystem Feedbacks in the Earth System [MarESys] , grant number  VH-NG-1301 ). R. Seferian  thanks the  ESM2025  project under the grant agreement No  101003536. The work reflects only the authors' views; the  European Commission  and their executive agency are not responsible for any use that may be made of the information the work contains.
Keywords used for the literature review The keywords used to run searches on original papers and review research on databases such as Elsevier, ResearchGate and Pubmed were taken from the following list: -Carbamazepine, micropollutant, xenobiotic, pharmaceuticals, PPCPs. -Products, metabolites. -Occurrence, quantification, toxicity. -Reaction pathways, degradation, removal, processes, advanced oxidation processes, water treatment. Based on these keywords, 97 papers published between 2000 and 2022 were selected. Among them, 39 concerned the TPs of CBZ.
Construction of the transformation products database The physicochemical characteristics of CBZ and its TPs were determined using  MarvinSketch [v22. 13. 0]  developed by ChemAxon© Ltd. (http://www. chemaxon. com). This software was used in particular to draw the chemical structure of TPs, to calculate molecular weights (MW), chemical formulas, polar surface areas (PSA) and van der Waals surface areas (WSA), and to predict pKa (T = 20 • C), logD (pH = 7) and the H-bond donor/acceptor count.
Data processing Figures and tables were created using RStudio interface [v2022. 07. 2] and R programing languagemore specifically, the ggplot library from the tidyverse packagebased on databases constructed from literature data. And the specific formattable package was used to create Table  1. The graphical visualization of CBZ transformation pathways into TPs, as shown in Fig. 10 , was generated using Gephi software [v0. 9. 5] for drawing maps and networks. For statistical analyses using box-and-whisker plots, a Student-t-test was run after verification of data normality by means of a Shapiro-Wilk test. Whenever normality was not demonstrated, a Wilcoxon-Mann-Whitney test was then carried out. Statistically significant values were defined as p-values of 0. 05 (*). When results were reported in several scientific papers, the list of the articles in which these results appeared is presented in the supplementary data.
TP toxicity The acute and chronic toxicity values of 31 TPs were found in the Fig. 9. Proportions of TPs (31 TPs considered) in each toxicity category. Acute toxicity (AT) and Chronic toxicity (CT), predicted by ECOSAR software, in three different aquatic organisms (fish, daphnid, green algae). The TPs were classified as not harmful (>100 mg⋅L -1 ; green), harmful (10-100 mg⋅L -1 ; yellow), toxic (1-10 mg⋅L -1 ; orange) and very toxic (<1 mg⋅L -1 ; red) according to the Globally Harmonized System of Classification and Labeling of Chemicals (GHS). Results are shown in terms of the proportion of TPs in each toxicity category  (Ali et al. , 2018; Bu et al. , 2018; Kråkström et al. , 2020; Wang et al. , 2022; Xu et al. , 2021). TPs previously appearing in Fig. 8  are written in their corresponding toxicity category.