dataesr/scientific-paragraphs-categorization huggingface dataset:
12 ± 0. 11 ‰;  Cameron et al. , 2009; Wang et al. , 2019; Wu et al. , 2022; Revels et al. , 2021). This range is consistent with the scarce river data reported for large rivers, ranging from + 0. 18 ‰ to + 1. 35 ‰  (Cameron and Vance, 2014; Revels et al. , 2021). There is no systematic pattern in dissolved δ 60 Ni related to the different river types. Similarly, no relationships exist between dissolved δ 60 Ni and major cations and anions (Fig. 2d-f ), or dissolved Ni concentrations (Fig. 3 ). However, the rivers from this dataset, together with the large rivers from  Cameron and Vance (2014) , seem to reveal a broad "boomerang-like" relationship, with a maximum δ 60 Ni at intermediate Ni concentrations. This maximum may indicate a maximum extent of Ni scavenging by secondary phases, a feature reminiscent of Li and its isotopes in the Amazon Basin (Fig.
7c ). Note that dissolved δ 60 Ni and δ 7 Li do not come from the same sampling expeditions  (Dellinger et al. , 2015; Revels et al. , 2021) , but both δ 60 Ni and δ 7 Li show contrasting patterns between mountain-fed rivers (such as the Solimões and Madeira) and the plain rivers (Trombetas, Tapajós and Negro). No relationship emerges with runoff, suggesting that the interaction time between solid and fluid does not control the extent of metal oxide formation (Fig. 7d ). Altogether, the lack of relationship between δ 60 Ni, δ 7 Li and W/D in the Mackenzie Basin might suggest that Ni and Li are not scavenged by the same secondary weathering phases (Fig. 7c ). In fact, Li isotopes are thought to trace clay formation, while evidence from the literature and the Ni isotope fractionation factors deduced from our dataset (Fig.
Summary and conclusions In this contribution, we present a comprehensive dataset of δ 60 Ni and Ni abundances in both the river dissolved and solid loads of the Mackenzie Basin, as well as in the neighbouring Nass and Skeena rivers. The river solid δ 60 Ni ranges from + 0. 08 to + 0. 26 ‰, whereas the river dissolved δ 60 Ni ranges from + 0. 44 to + 1. 22 ‰. While the river dissolved δ 60 Ni is significantly different from crustal silicate rocks, the δ 60 Ni in river solids exhibits a silicate rock signature. Dissolved δ 60 Ni does not show any relationship with common source tracers, thus negating a lithological control on the variation in river dissolved δ 60 Ni. In contrast, the Ni isotope differences between the river dissolved and solid loads are associated with elemental Ni partitioning between the dissolved and solid loads.
Acknowledgement The authors thank  Julien Bouchez ,  Jérôme Gaillardet  and  Robert G. Hilton  for providing the samples used in this study. This research was supported by  Swiss National Science Foundation  grant  200021-184873  to DV. Cécile Gautheron  is thanked for her efficient editorial handling of the paper. Gildas Ratié  and two anonymous reviewers are thanked for their constructive comments.
Paper Environmental Science: Processes & Impacts oxalate, gluconate and phosphate) were measured in transmission mode. The incident beam was monitored with a N 2 -lled gas ionization chamber. XANES spectra were analyzed using linear combination tting considering several footprint components in agreement with the local environment and geometry around Fe atoms (hydroxide as goethite, carboxylate as ferric citrate and phosphoryl as Fe(III) phosphate). EXAFS signals were analyzed up to a maximum k value of 12 Å -1. Multiple scans of 60 min per scan acquisition time were recorded at different spots of the sample to further avoid beam induced reactions. Data treatment and analysis have been performed using the ATHENA and VIPER soware, using a weighted k 3 $c(k) signal with a window ranging from 2.
Environmental Features of the Rearing Sites At each rearing site during 2018 and 2019, we placed a data logger (iButton, DS1923-F5# Hygrochron Temperature and Humidity Data Logger, iButtonLink, LLC, Whitewater, WI, U. S. A. ) in a tube at substrate level, which registered water temperature every 12 h. We recovered data loggers at the end of the experiment; unfortunately, the failure of some data loggers hampered the collection of data from some sites. After the cross-environment experiment, we kept cages without salamanders inside for 6 months in three of the epigean rearing sites and three of the cave rearing sites. We then used the pipe sampling technique  (Ficetola et al. , 2011)  to assess prey availability (average density (N/m 2 ) of macroinvertebrate prey) both inside and outside of the cages.
Ethical Note The collection and maintenance of larvae were performed in accordance with the Regional Law 10-31/03/2008 (Lombardy Region) and approved by the regional ethical committee (permit numbers F1. 0002091, T1. 2015. 0001053, T1. 2016. 0052349 and 18303_2019). At the end of the experiment, we released each larva at the site of collection. The experimental plan was designed following the PREPARE Guidelines; it adheres to ASAB/ABS Guidelines for the Use of Animals in Research. The study design was planned to minimize the number of fire salamander larvae used in the experiment; we performed the experiment across multiple years, minimizing the stress for the individuals and keeping statistical power. Rearing was performed as carefully as possible to maximize the reliability of data collection and minimize fire salamander larvae stress.
, 1997; Vandel & Bouillon, 1959). Our study provides evidence that, in environments with low trophic resources availability, the sit-and-wait predatory strategy may be insufficient to ensure capture of enough prey to meet basic energetic requirements, even when marginal costs are strongly reduced. Our study provides a useful resource for further studies comparing the intra-and interspecific foraging strategies of typical cave species and of species currently colonizing caves. For each site, habitat features, area and number of cages used during each year of experiment are reported. The table shows the maximum, minimum and average water temperature recorded by data loggers in the rearing sites used in 2018 and 2019 and the recording period in which rearing occurred. Note that in sites R26 and G152, the data are reported for 2018, while rearing occurred in 2019, when data loggers did not work.
In situ data In this study, we considered two sites from the FACE network in nitrogen-limited temperate forest ecosystems; Oak-Ridge The version of ORCHIDEE used in this study has yet to be optimised against Fluxnet data using a Bayesian framework, as it was done with previous nitrogen-free versions of the model (e. g. ,  Kuppel et al. , 2012; Peylin et al. , 2016). Therefore, we also considered TeBS and TeNE sites from the FLUXNET2015 dataset  (Pastorello et al. , 2020). This dataset provides gap-filled half-hourly meteorological data measured at each site (air temperature, humidity, pressure, wind speed, rainfall and snowfall rates, shortwave and longwave incoming radiation; see  Vuichard and Papale (2015) ). It also provides net carbon flux measurements, as such net ecosystems exchange (NEE) further split into gross primary production (GPP) and total ecosystem respiration (TER) following a classical night-time vs day-time flux partition  Lasslop et al.
This means that if the monitoring network is not wellsuited as in NWSA, it is imperative to use other data sources as, for example, the Copernicus Atmosphere Monitoring Service (CAMS) reanalysis dataset  (Inness et al. , 2019) , satellite products (e. g. , MODIS -NASA, 2023), and the GEOS-Chem reanalysis  (Bey et al. , 2001). CAMS has already been validated for PM 2. 5 in the region by  Casallas et al. (2022 -hereafter CCG22)  and IDEAM (2022) for other pollutants. Many studies have evaluated it in other regions (e. g. ,  Guevara et al. , 2021; Kuenan et al. , 2022) , showing acceptable results. Aerosol Optical Depth (AOD) from MODIS has been analyzed by the region by  Guevara-Luna et al. (2018) , and GEOS-Chem was first evaluated in NWSA by  Rodriguez-Villamizar et al. (2022)  with reasonable results, meaning that all these datasets could be used as tools to study pollution hotspots in NWSA.
In-situ measurements and data quality control This study employs hourly surface measurements of O 3 , PM 10 , PM 2. 5 , NO, NO 2 , and SO2 concentrations from the Colombian Air Quality Information Subsystem Database (SISAIRE, 2023) spanning 2012-01-31 to 2022-12-31 across thirty-two Colombian cities. The data is utilized for two main objectives: (i) conducting a temporal trend analysis of pollutants based on their spatial distribution, and (ii) validating the CAMS reanalysis for NWSA. Before data usage, Chauvenet's criterion, following CCG22 and influenced by  Lishu et al. (2015) , is employed to mitigate data uncertainty. The criterion, typically applied to parametric distributions, is adapted here to identify data three standard deviations above the mean. Subsequently, these marked values are cross-referenced with data from the three nearest surface measuring stations to ascertain if values result from regional patterns or possible sensor outliers.
Reanalysis dataset and validation The CAMS reanalysis dataset, spanning 8 complete years from 2014-01-01 to 2021-12-31, was acquired with a 3-hour temporal resolution and 0. 75 • spatial resolution. Validation involves comparing CAMS daily means (daily maximum for ozone) with pollutant averages (8-hour max daily mean for ozone) from available stations for each pollutant and city during 2021. Model validation scores, including Index Of Agreement (IOA), Spearman correlation (Rho), Factor of Two (FAC2), Normalized Mean Bias (NMB), Root Mean Square Error (RMSE), and Mean Bias (MB), along with categorical statistics such as Hit Rate (HIT), False Alarm Rate (FAR), and Proportion of Correctness (POC), are computed following  Celis et al. (2022). Benchmarks and thresholds are selected from previous studies  (Chai et al.
Analysis of air pollution trends For examining air pollution trends in NWSA, we harnessed monitoring station data already subjected to quality control processes outlined in Section 2. 1. 1. We initiated by conducting a multi-annual analysis for each pollutant, scrutinizing seasonal, monthly, and diurnal patterns, following CCG22's methodology, which draws from  Singh et al. (2021). Next, we computed monthly means to execute two analyses. Firstly, we employed Seasonal Trend decomposition using LOESS smoothing (STL) based on  Bigi and Ghermandi (2016). This method crafts a smoothed trend line to disentangle data into three components: seasonal, trend, and residual. Subsequently, using the monthly mean, a Generalized Least Squares (GLS) regression from  Brockwell and Davis (2002)  was fitted to quantify pollutants' linear trends, offering a comprehensive view of the region's air quality evolution.
Lagrangian framework The framework arises as a proposal for conducting robust LRT evaluations of pollutants. It is an alternative face to other models such as HYSPLIT which have different drawbacks that we are looking to overcome in this research. It adopts a Lagrangian approach, allowing for tracking and analyzing the evolution of plumes. Our framework also considers crucial aspects, including the spatial extent, concentration, and distance travel by the plumes. Besides, it offers versatility to be applied to multiple datasets such as the CAMS reanalysis dataset, as well as outputs resulting from different models (e. g. , WRF-Chem).
Tracker theory and design Our method employs a watershed algorithm as a Lagrangian tracker to discern and track pollution plumes to unveil their traits. We implement the watershed algorithm using Scikit-image version 0. 2  (Virtonen et al. , 2020)  in Python 3. 9. Initially, the algorithm segments the image, where each segment corresponds to a unique object or part based on distinct characteristics. By applying a threshold, the algorithm identifies and labels objects within image segments exceeding/lacking certain values. For instance, it can label zones where a pollutant exceeds a predefined threshold, which can be fixed or dynamically adjusted based on percentiles/magnitudes over time and space. The boundaries will be defined depending on the plume concentration akin to  Pouyaei et al. (2022).
Tracker application and analysis: CAMS reanalysis This tracker-based approach holds promise for application with diverse gridded datasets, including the publicly available CAMS reanalysis dataset, showcasing its potential for global use. While the results and discussion section dives into specifics, it's notable that CAMS struggles to accurately represent NO and NO 2 in the NWSA, leading us to exclude them from tracker analysis here. The Lagrangian tracker requires a threshold to distinguish pollution plumes. For PM 10 and PM 2. 5 , we employ the spatial and temporal 90th percentile (spatial and temporal for all pollutants) as a threshold, refined through multiple tests with the objective of capturing notable Amazonas and north Colombian events  (Mogollón-Sotelo et al. , 2021). Tracker assessment was conducted for both PM 2.
5 and PM 10 , but we present PM 2. 5 results as they align significantly. Similarly, the 90th percentile serves as the threshold for SO 2. Unlike other pollutants, SO 2 events are not well documented so multiple tests were applied with the idea of effectively capturing a significant number of events from volcanoes and wildfires. In contrast, O 3 analysis adopts the 70th percentile. This threshold accommodates on-land events and not only high Caribbean Sea O 3 plumes  (Cooper et al. , 2014) , and was refined through tests aiming to capture well known events in Bogotá,  Medellín, and Bucaramanga (Ballesteros-González et al. , 2020; Mendez-Espinosa et al. , 2020a , 2020b; Casallas et al. , 2023a Casallas et al. , , 2023b)). Our approach enables a comprehensive study using the CAMS reanalysis dataset for general events, supplemented by the WRF-Chem model for specific cases.
The Grell 3D convective parameterization  (Grell and Dévényi, 2002). The convective scheme is used since the inner-domain has a 6 km 2 resolution, being on the verge of the gray zone, this could be a source of error, but as shown by  Freitas et al. (2020)  the use of parameterized convection at 6 km 2 scales should not produce significant differences compared to a 3 km 2 simulation. Meteorological boundary conditions stem from the Global Forecast System (GFS) (NCEP, 2015) at 0. 25 • resolution with 40 vertical levels. Chemical initial conditions adopt the global anthropogenic emissions inventory EDGAR v4. 3. 1  (Crippa et al. , 2016)   et al. (2020). The biogenic inventory MEGAN v2. 1  (Guenther et al. , 2012)  operates at 0. 5 • resolution. Fire emissions employ the FINN v1. 5 database, available daily at 1 km 2 resolution  (Wiedinmyer et al.
, 2023a) , impeding robust satellite data  (Godłowska et al. , 2023)  assimilation in CAMS. However, pollutants other than NO x (i. e. , PM 10 , PM 2. 5 , O3, and SO 2 ) are well-captured by the CAMS dataset for temporal trends. MB and other statistical parameters closely align with benchmarks in most instances. Therefore, we omit NO x from further analysis in this research. Although spatial distribution of NO x is described, since concentrations   (Met Office, 2015). (g-h-i-j-k-l) Corresponds to the MB calculated for PM 10 , PM 2. 5 , O 3 , NO 2 , NO, and SO 2 , respectively. The horizontal reddashed lines indicate the benchmarks values which are adopted from previous studies  (Chai et al. , 2013; Gupta and Mohan, 2015; Emery et al. , 2017; CCG22)  and from the WHO (  2021 ) guidelines (see Tables  SM1  and SM2 ).
A similar lack of correlation is observed between the EIS trend and pollutant trends. Nonetheless, when monthly temperatures are high in the northern region, CVB, or Caquetá, it's accompanied by an increase in wildfires, leading to heightened pollution in certain cities. However, this connection primarily reflects inter-annual variations rather than a consistent tendency change. Notably, the tendency of wildfires is rising in the Amazon but not in the north and CVB (details in CCG22 SM). Additionally, data from airport gauges (IDEAM, 2022) in each city were utilized to minimize uncertainty related to meteorological variables, particularly precipitation. Both data sources yield the same conclusions. Crucially, prior to delving into city-specific trends, it's important to underscore that most pollutants experienced a decline during the COVID-19 lockdown.
Conclusions This paper analyzes the spatiotemporal variation and trends of particles and gaseous pollutants' concentrations, i. e. , PM 2. 5 , PM 10 , NO 2 , NO, SO 2 , and O 3 in NWSA, to better understand the phenomena related to the pollutant behavior (especially the peaks) and also to assess the effectiveness of policies and measures implemented. For this, we use insitu measurements and validate the CAMS Reanalysis with those measurements. CAMS dataset has a medium/good accuracy for PM, SO 2 , and O 3 , reproducing trends, variations, magnitudes, and extreme events in the study area. Nevertheless, CAMS fails to capture the magnitude of both NO and NO 2 , possibly because only three cities measure both pollutants. Still, the reanalysis of the NO and NO 2 pollution hotspots such as main cities and typical wildfire's locations.
(ii) The threshold could also produce uncertainties, so other filters have to be included to prevent capturing small events or short-lived events not associated with wildfires or volcanic activity. (iii) The results (and especially the composites) can be sensitive to the data resolution, principally in places with complex terrain that modifies the wind flow. The data also needs to have an hourly output to fully capture the behavior of the plumes, since a daily resolution could produce misleading results. Although this is not a limitation, it is one important characteristic to take into account when determining the dataset to be used, since the results could be sensitive to data resolution. Based on our research, we propose pollution reduction strategies for the NWSA region: (i) International Cooperation: Collaborate internationally to reduce wildfire risks through improved land use practices, reforestation, improve surveillance, monitoring, and territorial planning/development.
Acknowledgments First and foremost, we express our gratitude to one of the reviewers of the CCG22 work, whose comments on the HYSPLIT analyses served as a catalyst for the development and application of the Lagrangian framework presented in this study. We want to thank  Edward Camelo  and  Leo Durst  for their suggestions. We also thank  Sebastian K. Muller  for discussions on the Lagrangian tracker. Additionally, we are grateful to  Yang Chen  for discussing the significance of studying wildfires and air quality as compound events during the ICTP "Workshop  on Climate Information for Risk Assessment and Regional Adaptation from Global Scale Climate Projections to Local Scale Climate Hazards ". We would also like to thank two anonymous reviewers for their constructive comments. They were very important to improve and be more precise in the results of this manuscript.
Data availability We encourage the use of the Lagrangian tracker algorithm, which is available at the following link: https://github. com/Casicus94/Lagrangi an_air_quality/tree/main. The other scripts utilized to develop this research are available upon reasonable request to the lead author. The CAMS reanalysis  (Inness et al. , 2019)  data were downloaded from the Atmosphere Data Store, meanwhile the ERA5 reanalysis  (Hersbach et al. , 2023)  data were downloaded from the Climate Data Store, hotspots data from MODIS satellite were downloaded from NASA (2023) and the three datasets are publicly available. The WRF-Chem output used would be available for 5 years in the following link: https://drive. google. com/drive/u/0/folders/1knt3OmPqSuzhHhqSOCRDOiC3Lt FNi_8C. The in-situ station data can be downloaded from SISAIRE (2023)   or upon request to the lead author.
The second method is also applied to determine the 227 Ac extraction efficiency. We find a relatively wide-range of Ra and 227 Ac extraction efficiencies across the dataset (from 44. 8% to 99. 6% for Ra, and from 23. 7% to 77. 5% for 227 Ac). Overall, the yield of 227 Ac extraction is lower than that of Ra (mean value of 49. 3 ± 19. 0% for 227 Ac, n = 10, mean value of 79. 2 ± 10. 3% for Ra, n = 13, using the Mn-fiber method; and a mean value of 63. 9 ± 12. 5%, n = 11 using the A-B method). Our dataset suggests that the Ra extraction efficiencies using either the A-B method or the Mn-fiber method are in relatively good agreement. Consequently, the 223 Ra ex , 224 Ra ex and 228 Ra activities determined from the Mn-cartridges by applying the two Ra extraction yields are similar. We also show that the 227 Ac extraction efficiency can be estimated from the Ra extraction efficiency allowing the use of a single Mn-cartridge.
Estimation of 227 Ac extraction efficiency from E 1 (Ra) Based on our dataset, the Ra extraction efficiencies are slightly higher than the 227 Ac extraction efficiencies (Fig. 7 ). Here we evaluate if the 227 Ac extraction efficiency can be determined from the Ra extraction efficiency, using E 1 (Ra) as the reference. The E 2 ( 227 Ac): E 1 (Ra) ratio is on average 0. 64 ± 0. 23 (1SD, n = 10). This suggests that the 227 Ac extraction efficiency E 3 may be estimated as follows: E 3 ( 227 Ac) = 0. 64 × E 1 (Ra) (8) The 227 Ac activity in seawater Act SW ( 227 Ac) may thus be determined as follows:    6 )) as a function of the 227 Ac activities estimated by correcting Act A and Act B by the missing factor f miss following Kemnitz (2018) (Eq. (  7 )). The black dashed line shows the 1:1 relationship while the grey dotted line shows the linear regression forced by the origin.
Acknowledgements The authors thank  Hélène Planquette  and  Catherine Jeandel , PIs of the SWINGS project and chief scientist of the SWINGS cruise. We also thank the captain,  LDA  and GENAVIR officers, engineers, technicians and the crew of the  R/V Marion Dufresne  for their assistance during the SWINGS cruise. The authors also thank  Emmanuel de Saint-Léger  and  Fabien Pérault ,  Marion Lagarde ,  Nolwenn Lemaitre ,  Edwin Cotard ,  Frederic Planchon  for their help during the deployment of the in-situ pumps. We are grateful to  Elodie Kestenare ,  Frederic Vivier ,  Gérard Eldin ,  Sara Sergi ,  Corentin Clerc  and  Loyd Izard  for CTD data acquisition and preparation of Niskin bottles. We thank  Thomas Zambardi  for his help at the  LAFARA underground laboratory. The SWINGS project was supported by the  French Oceanographic Fleet ,  ANR (Agence Nationale de la Recherche ;  ANR) CNRS/ INSU (Centre Nationale de la Recherche Scientifique/Institut National des Sciences de l'Univers)  and  ISblue  ( ANR-17-EURE-0015 ).
2017; Estes et al. 2018; Zellweger et al. 2019) , making a better use of the ecological information contained in existing datasets should also be attempted in parallel. Structuring variation: a first step towards accurate representation of multidimensionality Our virtual experiment corresponds to an extreme case in which conspecific individuals have exactly the same response to environmental variation, which is unlikely to be the case under the joint effect of environmental and genetic variation in the field. However, while the intraspecific variation that is added as a noise around species means is not structured in space and time, IV, whether it is environmentally-or genetically-driven or both, is actually highly likely to be structured in space and time  (Girard-Tercieux et al. 2022). This structure could appear when IV results from spatially-structured environmental variables or from limited dispersion or local adaptation  (Marrot et al.