dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgments: The authors thank the civil volunteers enrolled by the  French Polar Institute  (project  136-SUBANTECO ) and  Matthieu Garnier , who contributed to the collection of specimens and data. The authors would like to thank  InEE-CNRS , who support the project  Long-term research on biodiversity in Antarctic and sub-Antarctic ecosystems. We are grateful to the  EcoChimie Platform (EcoChim)  from  UMS   OSUR 3343  for access to the facilities, which allowed us to perform biochemical measurements. Data were analyzed during the scientific retreat organized by the  ASICS team at St. Coulomb and St. Malo (Brittany, France) ,  26 May-4 June 2022.
This research was funded by the  ASICS  project ( ANR-20-EBI5-0004 ,  BiodivERsA ,  Biodiv-Clim  call  2019-2020 ) and the  French Polar Institute Paul-Emile Victor  (project  136-SUBANTECO ). LPR co-author was funded through the ASICS BiodivERsa South Africa that was provided by the  Department of Science and Innovation of the government of South Africa.
Data Availability Statement: The data presented in this study are openly available on FigShare at https://doi. org/10. 6084/m9. figshare. 21444753. v1 and on GitHub at https://github. com/CamilleCoux/ A_pacificum/tree/main/data. The R scripts from the analyses in this article are available on GitHub at https://github. com/CamilleCoux/A_pacificum.
These values capture within-species variation in the response trait and are typically assumed to be independent and normally distributed. As in  (1) , the values capture between-species correlation due to shared ancestry. This model was used by  Ives et al. (2007) , whose approach is now implemented in the R package phytools, for instance  (Revell, 2011). In their approach, the within-species error variance is estimated separately for each species and supplied by the user (as-is or via a sample from each species). These estimated variances are then "plugged-in" as true population variances, ignoring their estimation error. As an alternative to this "plug-in" approach, a joint estimation is used by several methods when a single observation per species is available ( only in (  2 )), such as the phylogenetic mixed model (PMM)  (Housworth et al.
, 2007). In that approach, a model is assumed for the evolution of all variables (rather than for residuals only), and between-species relationships are represented by phylogenetic covariances between traits instead of the coefficients. In addition, within-species relationships are represented by a multivariate phenotypic covariance matrix, whereas (2) has univariate within-species variation in the response only. For a list of implementations that account for withinspecies variation, see Table  7. 1 of Garamszegi (2014). All of them assume the phylogeny to be a tree. The Julia package PhyloNetworks  (Solís-Lemus et al. , 2017)  is currently the only available implementation for PCMs on a network. Prior to this work, PhyloNetworks could not account for within-species variation in the response trait other than indirectly via Pagel's model.
Specifically, we can test if hybridization is useful to explain residual variation beyond the variation explained by geographical predictors. Finally, we sought to quantify how modeling choices may impact conclusions for this dataset. As described below, choices included (1) using ML versus REML, (2) using a tree that ignores reticulation but has more taxa (therefore more data) versus using a network that better represents the phylogenetic signal but has fewer taxa, and (3) accounting for or ignoring within-taxon variation.
Morphology and geography data We obtained leaflet length and width, latitude, and elevation data for all 30 Polemonium morphs with molecular data. Data previously published for a subset of morphs  (Rose, 2021)  were combined with newly generated data obtained from imaged specimens from the Consortium of Intermountain Herbaria 1 , Consortium of Pacific Northwest Herbaria 2 , Consortium of California Herbaria 3 , or loans from other herbarium collections made to JPR. Images were measured using Fiji  (Schindelin et al. , 2012) , measuring multiple leaflets per specimen when feasible, and then av-eraging to obtain a specimen mean. For imaged specimens, if coordinates were present but elevation was missing, elevation was extracted from the WorldClim 2 elevation shapefile at 30-arc-second resolution  (Fick & Hijmans, 2017)  using the R package raster  (Hijmans, 2020).
Leaflet data was obtained for between 3 to 275 specimens per morph (Fig. 2 , 1757 specimens total). For the BM model, we used leaflet size from all 1757 imaged specimens, and we used the median latitude and elevation for each morph, calculated using all specimens, imaged or not ( specimens total, from 3 (latitude) and 5 (elevation) to per morph). For the BM model, we only used imaged specimens for which latitude and elevation data could be extracted (997 specimens total, 2-218 per morph).
Grant/Award Numbers: ANR-10-IDEX-0001-02 PSL, ANR-17-EURE-0017, EUR FrontCog; Investissements d'Avenir Edited by: Yoland Smith (but not with exteroceptive metacognition) and an association of autonomic function with exteroceptive metacognition (but not with fatigue). Furthermore, machine learning (elastic net regression) showed that individual fatigue scores could be predicted out-of-sample from our measurements, with questionnaire-based measures of interoceptive awareness and sleep quality as key predictors. Our results support theoretical concepts of interoception as an important factor for fatigue and demonstrate the general feasibility of predicting individual levels of fatigue from simple questionnaire-based measures of interoception and sleep. K E Y W O R D S allostatic self-efficacy, computational psychiatry, confidence, fatigue, interoception, metacognition, multiple sclerosis, perceptual decision-making.
Data collection started in June 2019 and was completed in October 2020. A total of 14 participants were included after the World Health Organisation declared the SARS-CoV2 (severe acute respiratory syndrome coronavirus 2) outbreak a global pandemic. In principle, it is possible that pandemic-related stress or biological consequences of SARS-CoV2 infections could act as confounders in that these variables might have influenced both levels of experienced fatigue and interoception and/or metacognition. Since we did not collect any data about pandemic-related stress or infections in our patients, we could not account for these potential confounders in our analyses.
| Study procedures We tested several hypotheses about statistical relationships between self-report measures of fatigue on the one hand and questionnaire-based measures of interoceptive awareness, measures of autonomic regulation and of metacognition on the other hand while controlling for age, sex and medication as potential confounds. We provide a detailed account of the hypotheses below. These hypotheses and the statistical procedures to test them were defined prior to data analysis and are described in a prespecified, time-stamped analysis plan available online (https://gitlab. ethz. ch/tnu/analysisplans/rouault_imefa_analysis_plan). Section 3 below indicates whenever procedures deviated from this preregistered analysis plan (e. g. additional analyses). The study consisted of two sessions on separate days.
| Questionnaires Our analyses used data from the following questionnaires. To assess individual fatigue levels, we used both the Modified Fatigue Impact Scale (MFIS)  (Larson, 2013)  and the Fatigue Severity Scale (FSS)  (Krupp et al. , 1989). This allowed us to verify whether our results were robust to the specific construct of fatigue (sensitivity analysis). To measure the feeling of being in homeostasis and control, we used two subscales ('Not-Worrying' and 'Trusting') from the MAIA questionnaire  (Mehling et al. , 2012). The Hospital Anxiety and Depression Scale (HADS)  (Zigmond & Snaith, 1983 ) was used to assess symptoms of depression. In our context, this is a preferred screening tool for depression since, in contrast to other questionnaires of depressive symptoms, it does not include questions relating to fatigue.
| Physiological assessments of autonomic function Concerning physiological tests of autonomic function, the following measurements were obtained: heart rate variability (HRV, determined by computing the root mean square of successive differences [RMSSD] during deep breathing), changes in blood pressure (ΔBP) and heart rate (ΔHR) when standing up after resting in supine position for 10 min, sudomotor activity (Sudoscan, Impeto Medical, France), body temperature (via auricular measurement in the right ear) and sympathetic skin response (SSR). Due to concerns about the quality of SSR measurements, we did not include these data in our analyses. F I G U R E 1 Summary of measurements of metacognition, fatigue and interoception. Participants completed a number of self-report questionnaires, including two fatigue questionnaires, the Modified Fatigue Impact Scale (MFIS) and the Fatigue Severity Scale (FSS).
Metacognitive bias (or 'confidence level') refers to one's tendency to rate confidence higher or lower. In contrast, metacognitive efficiency refers to one's ability to discriminate between correct and incorrect responses, controlling for the influence of task performance (d 0 ). This index of metacognitive efficiency is based on meta-d 0  (Maniscalco & Lau, 2012) , a metric developed in a manner analogous to the classical d 0 index from signal detection theory but reflecting instead how much information, in signal-to-noise units, is available for metacognitive decisions. Specifically, we extracted for each participant their first-order performance (percent correct and perceptual sensitivity d 0 ), metacognitive efficiency (meta-d 0 /d 0 ) and metacognitive bias (i. e. confidence level), using the HMeta-d toolbox (https://github.
| Statistical analysis plan Our analyses were prespecified in a time-stamped analysis plan available online (https://gitlab. ethz. ch/tnu/ analysis-plans/rouault_imefa_analysis_plan). During the analysis, we recognised the need for some adjustments and additional analyses. These deviations from our prespecified analysis plan are indicated in Section 3 below. We tested statistical relations between self-report measures of fatigue on the one hand and measures of autonomic regulation, metacognition and questionnairebased measures of interoceptive awareness on the other hand, while controlling for potential confounds (such as age, sex and medication). We adopted two general analysis approaches: a. hypothesis-based approaches which tested a priori hypotheses about the relation of fatigue to interoception, autonomic regulation and metacognition using general linear models (GLM), and b.
| Hypothesis-based approach For testing a set of specific a priori hypotheses, we performed null hypothesis testing using general linear models (GLM), implemented in MATLAB (glmfit function). We chose a significance level of α = 0. 05; where necessary, we corrected for multiple comparisons using a Benjamini-Hochberg (BH) procedure to control the false discovery rate (FDR) at 5%. In all regression analyes, regressors representing potential confounds included age, sex, disease duration (date of data acquisition minus date of initial diagnosis) and medication. In our prespecified analysis plan, we had originally envisaged to use dose and drug type as confound regressors. However, the patients in our sample took so many different types of medication that the original plan would have led to a prohibitively large number of regressors in relation to the number of data points.
This analysis included all autonomic, questionnaire-based interoceptive and task-based metacognitive measurements and aimed at identifying the most meaningful predictor(s) of fatigue scores without a priori hypotheses or preselection of regressors. Elastic net regularisation introduces two penalty terms to the ordinary least squares objective function that combine properties of lasso (L1 penalty) and ridge regression (L2 penalty), allowing selection of a solution with particular properties. In particular, for problems with few data points compared with the number of regressors, this avoids overfitting, thus enhancing the accuracy of the predictors, through automatic variable selection and shrinkage of large regression coefficients. In our application of elastic net regression implemented in scikit-learn, we z-scored the predictors (regressors) and mean-centred the outcome variable (fatigue scores).
ACKNOWLEDGEMENTS We would like to thank  Jakob Heinzle  for helpful discussions about statistical analyses,  Fabien Vinckier  for early discussions about the protocol design,  Nathalie Appenzeller  for help with data checks and the  Swiss Multiple Sclerosis Register  for assistance with recruitment. MR is the beneficiary of a postdoctoral fellowship from the  AXA Research Fund. KES  acknowledges funding by the  René and Susanne Braginsky Foundation  and the  University of Zurich. ZMM acknowledges support by the  Wilhelm Schulthess Foundation , the  Olga Mayenfisch Foundation , and the  Swiss MS Society. MR work was also supported by  La Fondation des Treilles  and by a department-wide grant from the  Agence Nationale de la Recherche  ( ANR-17-EURE-0017 ,  EUR FrontCog). This work has received support under the  programme 'Investissements d'Avenir ' launched by the  French Government  and implemented by  ANR  ( ANR-10-IDEX-0001-02 PSL ).
DATA AVAILABILITY STATEMENT All participants were asked for consent that their pseudonymised data could be shared; for those participants who agreed (68 out of 71 participants), the data are available on the ETH Research Collection (www. research-collection. ethz. ch/handle/20. 500. 11850/609819). The MATLAB code for the GLM analyses is available at https://www. github. com/marionrouault/imefa/, and the Python code for the elastic net analysis is available at https://gitlab. ethz. ch/ hermang/rouaultetal_fatigue_elastic_net.
(2017)  who computed the e-folding using both the theoretical formalism of  Hill and Vasyliūnas (2002)  and UV observations from the Hubble Space Telescope. These studies show that the tail brightness decays exponentially, with a theoretical e-folding value of 6° and an observational value of 14°. To compute our e-folding values, an exponential fit is made after averaging and re-binning the data in 15° intervals. Our calculations find an e-folding value of 7. 4°, which is in good agreement with the previously stated values. The electron characteristic energy also tends to decrease as Juno crossed the Europa flux tube further down in the tail (Figure  2e ). The size of the interaction region in the equatorial plane was estimated by magnetically projecting the position of Juno onto Europa's orbital plane.
Data Availability Statement JADE data used in this study are publicly available on the Planetary Data System, node Planetary Plasma Interactions website (PDS-PPI), in the dataset JNO-J/SW-JAD-3-CALIBRATED-V1. 0 (https://doi. org/10. 17189/1519715). UVS data are archived on the Atmospheres node (PPI-ATM). Part of the analysis have been done by the AMDA software (http://amda. cdpp. eu) provided by CDPP (http://www. cdpp. eu/) and the CLWEB software (http://clweb. irap. omp. eu) developed by E. Penou at IRAP.
Multiple scattering model The analytical model based on the quasi-single-scattering small-angle approximation is used to simulate oceanic HSRL signals efficiently  [55]. The analytical model is applied to the forward simulation of triple-FOV HSRL signals and the further iterative retrieval process, achieving more credible simplification than the traditional methods. In the analytical model, the coaxial molecular signal profile of HSRL can be expressed as where F represents the forward simulation of HSRL molecular signals by analytical model; C 0 is the system constant; a 1 …a i , b 1 …b i , and ξ 1 …ξ i are whole profiles of a, b, and ξ. This means that once the lidar system parameters are determined, the molecular signal can be written as an equation of a, b, and ξ. Data from a semianalytical MC were used to validate the performance of the retrieval algorithm in this work.
In situ measurements An optical package, which included WETLabs AC-S and HOBILabs HS6P, measuring in situ optical properties was deployed at specific locations (stations) lowered with a winch. WETLabs AC-S can measure the spectral absorption and beam attenuation coefficients (here, we use only the values at 532 nm). The particulate backscattering coefficient at 532 nm was obtained from the particulate backscattering at 510 nm from HOBILabs HS6P, assuming b bp (λ) = b bp (λ 0 )λ 0 /λ  [57]. ξ is calculated by substituting the in situ values of b p and b bp into Eq. 5. All in situ data were binned to a depth resolution of 1 m. It should be noted that, in situ ξ are converted from in situ measurements of the particulate backscattering fraction BF of the seawater. Therefore, for the rigor, we also converted retrieved ξ to BF and compared them with in situ values in the Supplementary Materials, Fig.
Acknowledgments The authors want to thank insightful discussions from  Dr. M. Behrenfeld  at  Oregon State University  about lidar data and marine science. We thank  NASA Ocean Biology Processing Group, USA, National Marine Data Information Center, China, Remote Sensing Systems, USA , and  National Geophysical Data Center, USA , for the ocean color and other auxiliary data. We thank the captain and all the crews of  R/V Runjiang No. 1 for the assistance and support in the field experiment, and scientists at  Second Institute of Oceanography, China , for collecting in situ data. Funding: This study was supported by the  National Key Research and Development Program of China  ( 2022YFB3901704 ); the  Excellent Young Scientist Program of Zhejiang Provincial Natural Science Foundation of China  ( LR19D050001  and  LQ23F050011 ); the  Fundamental Research Funds for the Central Universities  ( 2021XZZX019 ); the  National Natural Science Foundation of China (NSFC)  ( 62205289 ); the  Scientific Research Foundation for Talent Introduction of Zhejiang University Ningbo Campus  ( 20201203Z0175  and  20201203-Z0177 ); the  Ningbo Natural Science Foundation  ( 2022J153  and  2022J154 ); the  State Key Laboratory of Modern Optical Instrumentation Innovation Program ; and the  Zhejiang University Global Partnership Fund.
Data Availability The digital topographic data from the ETOPO1 Global Relief Model published by the National Geophysical Data Center are available at http://www. ngdc. noaa. gov/mgg/global/global. html. The tide height data from the National Marine Data and Information Service are available at http://global-tide. nmdis. org. cn. The Chl and PAR from Level-3 products of MODIS are available at https://oceancolor. gsfc. nasa. gov/l3. The wind field data from CCMP Wind Vector Analysis Product (L3, version 2. 0) are available at https://data. remss. com/ccmp. The code of the random forest method is available at https://github. com/ karpathy/Random-Forest-Matlab. The other data are available from the corresponding author upon reasonable request.
Globalization challenges sustainability by intensifying the ecological and economic impacts of biological invasions. These impacts may be unevenly distributed worldwide, with costs disproportionately incurred by a few regions. We identify economic cost distributions of invasions among origin and recipient countries and continents and determine socio-economic and biodiversity-related predictors of cost dynamics. Using data filtered from the InvaCost database, which inevitably includes geographic biases in cost reporting, we found that recorded costly invasive alien species have originated from almost all regions, most frequently causing impacts to Europe. In terms of cost magnitude, reported monetary costs predominantly resulted from species with origins in Asia impacting North America. High reported cost linkages (flows) between species' native countries and their invaded countries were related to proxies of shared environments and shared trade history.
Identification of socio-economic and environmental predictors of invasion costs could also help to inform proactive management  [25]. The InvaCost database (Supplementary Note 1) documents globally reported monetary costs of IAS. Notwithstanding known regional biases in the reporting of costs, it allows comparisons of standardized costs at taxonomic  [11] , sectoral  [15] , regional  [15] , and global levels  [5]. To investigate the spatial distribution of senders and receivers of sustainability challenges triggered by invasions, we quantify the monetary burden within and flowing between sender and recipient regions (continents or countries). Specifically, we examine: (i) whether some continents or countries send or receive a disproportionate amount of economically costly IAS, and costs associated with those IAS; and (ii) whether socioeconomic and environmental variables predict cost flows between country pairs.
Continent-level patterns Numbers of IAS sent and their reported costs were unevenly distributed across continents (Figure  1 ). Note that the same was true for reporting effort, and that our results should be interpreted with this in mind (Figure  1a ). Together, the Northern continents (Europe, Asia and North America) both sent (67%) and received (66%) the majority of IAS. "IAS" here, and throughout the results, refers only to non-domesticated species, with costs in the InvaCost database, and for which we could identify ≥1 native continent or country (Figure  2 , see Methods). The Northern continents also sent 82% and received 95% of the total reported invasion cost in our dataset ($467 billion from 459 IAS; all costs reported herein are in 2017 United States of America [USA] dollars). Our dataset did not contain any costs attributable to single species in Antarctica.
These regional patterns were similar when accounting for research effort (number of publications in our dataset) as a simple proxy for capacity to report IAS costs, although South America became a net receiver (Figure  1d ). Furthermore, these patterns are relatively insensitive to additional data, as substantial increases in research effort would be required to override these results (446-1,446% to override the top sender and 729-5,267% to override the top receiver, Supplementary Table  1 ). While we do not focus on temporal trends, we note that across continents, species and reported costs (both raw and per publication) sent and received tended to increase over time (Supplementary Note 2; Extended Data Figures  2 3 4 ). Reported cost flows among continents according to cost types and activity sectors are shown in Supplementary Tables  2 3.
5c ). Six of the top 10 pairwise relationships included the USA at the receiving end (seven when corrected by research effort). South Africa was a top receiver of costs from Australia (for raw reported costs only), while Canada was a top receiver from China, and China was a top receiver from Brazil and Colombia (for both raw reported costs and cost per publication). The top 10 was very similar when considering cost flows per publication (Extended Data Fig. 5 ). Additional analyses including species without countrylevel origin information changed the top 10 receiver countries, resulting in several more entries from Asia (Supplementary Figure  1 ). Of the 223 countries in our dataset, only 17 were net receivers (that is, reported costs incurred from IAS in these countries were greater than reported costs of IAS native to these countries).