dataesr/scientific-paragraphs-categorization huggingface dataset:
Hydrological monitoring Groundwater levels are measured via continuous pressure transducers across a network of 12 wells (Fig. 1 ). A U. S. Geological Survey gauging station (#11475560) monitors streamflow every 15 minutes and is located approximately 30 m upstream of the collection point for water chemistry. Precipitation data are collected across a network of tipping bucket rain gauges and distributed by the Eel River Critical Zone Observatory (https://dendra. science/). Historical precipitation for the Elder Creek watershed comes from PRISM  (Daly et al. , 2008 (Daly et al. , , 2015)). Time-domain transmission (TDT) sensors along one of the VMS sleeves provide records of water content and temperature at 15minute intervals. To approximate time-variable rock moisture storage in the root zone, we use relative moisture contents measured via TDT sensors at 1.
Geochemical analyses All water isotope samples were analyzed at the University of California, Berkeley Center for Stable Isotope Biogeochemistry. Samples were run via Isotope Ratio Mass Spectrometry on a Thermo Delta PLUS XL instrument. Data are reported in standard delta per-mille notation relative to Vienna Standard Mean Ocean Water (VSMOW) δ D = ⎛ ⎝ 2 H 1 H sample 2 H 1 H V SM O W -1 ⎞ ⎠ × 1000, (2) where a similar expression can be written for stable oxygen isotopes (i. e. , 18 O and 16 O). Precision of long-term VSMOW measurements are on the order of ±1. 0 for δD and ±0. 11 for δ 18 O  (Oshun et al. , 2016). These data are available as supplementary material. The concentrations of cations and anions were measured at the PARI analytical platform of the Institut de Physique du Globe de Paris (IPGP) with an Agilent 7900 quadrupole ICP-MS and a Dionex DX-120 ion chromatograph, respectively.
Samples annotated with an asterisk (*) have been previously reported in  (Golla et al. , 2021)  for cation and SiO 2 concentrations and δ 7 Li values. Stream discharge values for Elder Creek samples are estimated as the nearest measurement in the 15-minute resolution data set from the U. S. Geological Survey gauging station #11475560. The 2σ associated to isotope measurements are based on a set of triplicate measurements. The analytical precision associated with elemental concentrations is typically ≤5% based on long-term measurements of the river water reference material  Canada). The accuracy of Li isotope analyses can be assessed from the sample-specific 2σ of replicate measurements tabulated below and from the 2σ of repeated analyses of the OSIL Atlantic seawater reference material (±0.
, 2014) , although we see no evidence of this for other solutes. While there are no historical data to reference, the C-Q behavior of Li appears to generally follow that of the major geogenic solutes, particularly silica. Clearly, a wealth of information is contained within these storm data and in relation to the long-term records for these solutes. Here, we utilize these data specifically to analyze the extent to which high-intensity events are extrapolations of long-term behavior, and hence should ultimately reflect an expansion of the same underlying water-rock-life weathering reactions to more extreme conditions.
Conclusion This study merges a wide variety of data and models to arrive at a coherent analysis of the export of rock-derived solutes by fluids draining through an actively weathering, upland hillslope during an intense storm and of the emergence of stream geochemical signatures across the storm hydrograph. An atmospheric river delivered an intense period of infiltration that combined with the shallow layers of a large volume of pre-existing rock moisture stored in the bedrock vadose zone above the water table. The displacement of this fluid caused the water table to rise and groundwater flow rates to increase, yet the underlying water-rock reactions producing rock-derived solutes within the interior of the hillslope remained consistent. Through a series of model exercises, we show that combined mineral dissolution and precipitation reactions are necessary to explain the δ 7 Li data, ultimately supporting application of an isotope-enabled reactive transport model previ- The combination of our model results and physical observations demonstrates a clear sensitivity of Li signatures to changes in subsurface flow as the hillslope fills during a large storm event.
Acknowledgements We thank editor  Andrew Jacobson  and two anonymous reviewers for their thoughtful comments and constructive reviews. We are grateful to  Bill Dietrich  for providing helpful feedback on an earlier version of the manuscript. We thank  Pascale Louvat ,  Caroline Gorge , and  Pierre Burckel  from the  PARI analytical platform of IPGP ,  Micheal Henehan ,  Jutta Schlegel , and  Josefine Buhk  of the  HELGES laboratory at the German Research Centre for Geosciences, Helmholtz Centre Potsdam  for assistance with lithium isotope analyses and  Gunnar Reith  and  Will Speiser  for help with sample collection. This research was partially funded by the  People Programme  ( Marie Curie Actions ) of the  European Union Seventh Framework Programme   FP7 /2007-2013/ under REA agreement [608069] (ITN "IsoNose"), the  IPGP multidisciplinary program PARI , and the  Region île-de-France SESAME  Grant No.
Data availability Hydrological monitoring data are available on Dendra (https:// dendra. science/) and the U. S. Geological Survey National Water Information System (https://waterdata. usgs. gov /nwis). All other materials can be accessed on CUAHSI HydroShare: Golla, J. , J. Bouchez, M. Kuessner, D. M. Rempe, J. Druhan (2022). Accompanying data to Golla et al. (2022, EPSL)  "Subsurface weathering signatures in stream chemistry during an intense storm", HydroShare, https:// doi. org /10. 4211 /hs. d6e258cd5cb74bf18a4ef90177b4cd1d.
These data from the Alps have been part of a larger database used to argue for global accelerations to continental margins linked to increased erosive capacity for the climate in late Cenozoic times  (Molnar, 2004; Peizhen et al. , 2001). Whether changes in sediment yield due to climatic or tectonic forcings are preserved in the oceanic records of sediment accumulation depends largely on the rates of generation of accommodation through subsidence in the foreland basin  (Allen et al. , 2013). In this respect, foreland basins act to buffer the link between the mountains and the oceans. During the early stages of mountain growth, flexural subsidence generates more than enough space to accommodate the sediment flux from the range, and so deep-water, 'underfilled' conditions dominate in the foreland basin as the orogenically sourced material is trapped in the surrounding troughs.
| Model algorithm We have shown that for both the Western European Alps and the Northern Pyrenees, there appears to have been a first-order trend between the timing of post-orogenesis, the cessation of sediment deposition in foreland basins and the acceleration of sediment discharge to distal depocentres. Here, we explore the link between crustal thickening, topographic growth, foreland basin flexure and sediment flux in order to evaluate the impact on sediment dispersal during the onset of post-orogenic decay of a mountain belt/foreland basin system. Our orogenic system (i. e. mountain range and foreland basin) is modelled using the two-dimensional landscape evolution model Fastscape  (Braun & Willett, 2013) , available through the Xarray-Simlab package (Bovy, 2020). We take full advantage of model equations that simulate erosion, transport and deposition in both continental and marine domains  (Yuan, Braun, Guerit, Simon et al.
Large-sample studies  [20]  draw insights from a large collection of observation points. These have high potential also to be used for the study of human influences on streamflow drought, as shown by  [21]  and  [9]. These studies, however, rely on datasets with good-quality meta-data on human influences. With the CAMELS datasets  [22] [23] [24]  this information is becoming increasingly available in more countries, but still global coverage is limited. In many regions, local expert knowledge is still crucial to understand the timing, location, scale, and type of human influences in a catchment. This analysis is time-consuming and as a results often only one or a few cases are analysed. A literature review of 37 peer-reviewed case studies published between 2009 and 2021 (table 1; supplementary material S1 available online at stacks.
Case studies We collated 28 empirical case studies globally (figures 1(a) and (b)) that fit the selection criteria of this study (human influence present, time series longer than ten years, naturalisation data available). These cases cover all continents, with the highest concentration in Europe, and have a large spread in climate characteristics (figures 1(c) and (d)) and catchment sizes (figure  1 (e) and table  S2 ). Quantitative and qualitative contextual information for these cases was obtained from local expert knowledge and reports (summarised in tables S2 and  S3 ). Based on this information, the cases were grouped into four categories of human activities (figure  1(f) ). Dominant primary categories of human activity are abstraction from groundwater and/or surface water and water storage and release with reservoir infrastructure.
Drought analysis, comparison and human-drought types A consistent drought analysis methodology was then applied to both human-influenced and benchmark time series. Drought events were identified as periods during which the discharge is below the benchmark Q80 threshold (monthly 80th percentile of benchmark discharge, the value exceeded 80% of time)  [32] [33] [34]. Only droughts with durations of at least two months were considered. For all case studies, the threshold was generated from the benchmark data based on the longest time period available. Four different drought characteristics were calculated: total time in drought over the analysis period, average duration per drought event, total drought deficit over the analysis period, average deficit per drought event. We selected these because these are reported most in published literature (table  1 ) and because they are important indicators in water management indicating drought duration and severity  [35].
The classification of cases based on expert information required simplification of detailed and contextualised information, resulting in some uncertainty that was minimised through iterative discussion with data providers and local experts. Largest uncertainties are probably present in the classification of land use change, which is likely present in more cases than indicated in table  S3 , but often limited local information about these changes was available. The humaninfluence categories and water/land use purposes may not always be complete as information to characterise cases fully may be missing. There is uncertainty also in the benchmark datasets used for comparison with the human-influenced streamflow data. In this study, three different wellknown approaches were used  [31] , i. e. modelling, paired-catchment, and upstream-downstream comparison, and these were applied according to local data availability and type of human influence  [31].
Ways forward We see this paper as an important first step in globalscale comparison of observation-based cases on the human influence on streamflow drought. This collection of cases will be included as IAHS Panta Rhei benchmark dataset  [58]. In future work, more cases could be added to this dataset and the same analysis could be done to compare more cases, especially in areas that had limited coverage in this study. This would require good-quality data. Not only trustworthy observed and naturalised data over a long period of record are needed, but also detailed contextual knowledge on the human activities is crucial for the interpretation of the data  [59, 60]. In our study, it was a challenge to reach 28 cases, but we see strong potential for future studies with more cases. One way to do this is via the development of benchmark  [58]  and large-sample datasets  [20].
Acknowledgments This collection of case studies has been a community effort of the  IAHS Panta Rhei working group 'Drought in the Anthropocene'. We thank all data providers and MSc and BSc students who supported part of the data collection and analysis of specific cases. The paper benefitted from discussion with numerous colleagues over several years. This research supports the work of  UNESCO EURO FRIEND-Water  and the  IAHS Panta Rhei  programs. Data for case 4 Starohorský potok were provided by  Slovak Hydrometerorological Institute. Data for case 7 Upper-Guardiana were provided by the  Guadiana Water Authority. Data for case 12 Fiumicello were provided by  RomagnaAcque-Società delle Fonti. Data  for cases 16 Svitava and 17 Bílina were provided by  L Kašpárek  ( WRI, Czech Republic ). Data for case 19 Banabuiú were provided by the  Foundation of Meteorology and Water Resources of Ceará (FUNCEME).
F Hendricks  (EDF R&D) and  E Sauquet  ( INRAE, RiverLy ) provided background information for case 20 Ariège. SR and AVL were supported by  NWO Rubicon  ( 2004/08338/ALW ). AVL receives funding from the  European Research Council  project  PerfectSTORM  ( ERC-2020-StG 948601 ). HVL is supported by the  EU -funded  ANYWHERE  project (Grant Agreement No. 700099 ). NW was supported by  NWO   Rubicon 825. 15. 003  and  NWO 016. Veni. 181. 049. GDB was partly funded by the  European Research Council  ( Consolidator Grant ,  HydroSocialExtremes  project n. 771678).
Data Availability Statement The InSight event catalog V11 (comprising all events, including phase picks until April 2022) and waveform data are available from the IRIS-DMC, NASA-PDS, SEIS-InSight data portal and IPGP data center (InSight Mars SEIS Data Service, 2019a; InSight Mars SEIS Data Service, 2019b; InSight Marsquake Service, 2022). Seismic waveforms are also available from the NASA PDS (National Aeronautics and Space Administration Planetary Data System) (InSight Mars SEIS Data Service, 2019a). The data were processed with ObsPy  (Beyreuther et al. , 2010) , NumPy  (Harris et al. , 2020)  and Scipy  (Virtanen et al. , 2020) , and visualizations were created with Matplotlib  (Hunter, 2007).
Acknowledgments The authors are grateful to Jeroen Ritsema for a very constructive review that led to an improved manuscript. Comments by an anonymous reviewer were also appreciated as were those of Sanne Cottaar on an earlier version of this manuscript. Andrew Dombard is thanked for editorial handling. C. D. , A. K. , and D. G. would like to acknowledge support from ETH through the ETH+ funding scheme (ETH+02 19-1: "Planet Mars"). The authors acknowledge the NASA, CNES, partner agencies and institutions (UKSA, SSO, DLR, JPL, IPGP-CNRS, ETHZ, ICL, MPS-MPG), and the operators of JPL, SISMOC, MSDS, IRIS-DMC, and PDS for providing SEED SEIS data. Finally, the authors thank Géraldine Zenhäusern for providing us with the Martian seismicity location map. This is InSight contribution 269.
Fluorescence widefield imaging arrangement used Hg lamp (Olympus USH-1030L) light filtered at 438 ± 12 nm and the images were captured by a camera (Photonic Science CCD). Using confocal microscopy, the emitter was excited with a 405 nm pulsed laser (PicoQuant LDH series with 50 ps temporal width and operating at 2. 5 MHz) to measure the photon statistics, and the lithography was performed by a spatially modified 473 nm CW diode laser (LY473III-100). Time-resolved photoluminescence and time-correlated single-photon counting data was obtained after filtering the emission spectrally by a 630 ± 46 nm bandpass filter (Semrock 630/92 nm BrightLine) and spatially by a 150 µm pinhole, and using a PicoHarp300 photon counting module and two Micro Photon Devices PDM series single-photon avalanche photodiodes in a Hanbury-Brown and Twiss configuration.
Acknowledgements The authors thank  Jean-Paul Hugonin  for insightful discussions,  Willy Daney de Marcillac  for spectrometric measurements,  Loic Becerra  and  Stéphan Suffit  for vapor deposition, and  Bruno Gallas  for ellipsometry experiments. This work was supported by  DIM NanoK  funding through the project  PATCH  and by  ANR DELIGHT. Note: The figures were reset in higher quality on March 17, 2022, after initial publication online.
Similarly, in this work we utilize the full Pantheon+ sample of 1550 SNe Ia in combination with the R22 Cepheid host distances to show the impact of cosmological models with more freedom than those used in R22 as well as the impact of SN-related systematic uncertainties on inferences of H 0. An important aspect of this work is the public release of the data and simulations used here that allow for the reproduction of multiple different stages of this analysis. In Appendix C, we present the numerous products that will be made available, including SN distances, redshifts, uncertainties, covariance, and extensive SNANA simulations  (Kessler et al. 2009)  of the data that model astrophysical effects, cosmological effects, and the observation/telescope effects of each survey down to the level of cadence, weather history, etc.
We encourage the community to validate alternate analyses of the publicly released Pantheon+ sample on these simulations. The structure of the paper is as follows. In Section 2, we describe the methodology from fitting SN light curves to constraining cosmological parameters. Section 3 summarizes all of the inputs to the analysis including the data sample, calibration, and redshifts. In Section 4, we describe the cosmological results. Sections 5 and 6 are our discussions and conclusions, respectively.
Host-galaxy Properties Purpose: The observed host-galaxy mass versus SN luminosity relation is used to standardize the SN Ia brightnesses in two ways. First, simulations of the data set include correlations between SN color and SN stretch and host properties such as dust as a function of host mass, following  Popovic et al. (2021a). Second, a further residual correction is applied in the Tripp Equation (1) where the "mass step" γ is fit in the BBC stage. Baseline: The host-galaxy stellar masses are presented by S22 and references therein. Masses are determined for all host galaxies, and star formation rates and morphologies are also included the low-z sample. In the baseline analysis, we apply the mass step at 10 10 M e following Pantheon and DES3YR. Systematics: Several independent analyses  (Sullivan et al.
LOW-z data do not provide such metadata, and thus the cadence and noise properties are extracted from the data as described in  Kessler et al. (2019a)  following the procedure developed by  Scolnic et al. (2018b) , which assumes that the LOW-z subset of SNe is magnitude-limited. These are simulations of the CfA and CSP samples, but not of the newer samples included in this work (LOSS, SOUSA, and CNIa0. 02), thereby implicitly assuming that the CfA and CSP samples have similar selection effects and therefore distance biases as the newer additions. To simulate SN-host correlations, a catalog of host-galaxy properties and specifically their stellar-mass distributions is taken from  Popovic et al. (2021b). The simulations used for bias corrections for all surveys are performed in ΛCDM (w = -1.
2). Without applying any bias corrections, we find a significant step in sSFR (across the median sSFR) of 0. 031 ± 0. 011. However, after applying the nominal set of dust and mass-based bias corrections (BS21) used in this analysis, we find a step in sSFR of 0. 008 ± 0. 011, consistent with zero. This is likely due to galaxy properties (i. e. , stellar mass) being linked to dust properties, and that applying a dust-mass correction is accounting for most, if not all, of the correlations with sSFR and is also tracing the dust distribution. Going forward, statistical constraints on w and Ω M from SNe will improve significantly owing to upcoming data sets from SN It is likely that these future data sets will improve the statistical precision by a factor of 100  (Scolnic et al. 2018a). The size of systematic errors on cosmological parameter estimates matched the statistical errors for JLA and the original Pantheon.
g. ,  Regnault et al. 2015; Stubbs & Brown 2015; Narayan et al. 2019). There is also ongoing work (G. Taylor et al. 2022, in preparation)  to train the SALT2 model with more photometric systems, which has already shown promising improvements to systematic uncertainties and the ability to constrain the rest-frame U band. The systematic from the redshift-measurement floor has the potential to be reduced using improved cosmology fitting methodology, although the extent to which the data itself can constrain the size of this floor remains unproven. Alternatively, future large surveys can use multiple spectroscopic instruments and redshifting codes to mitigate potential sources of redshiftmeasurement bias. The Pantheon+ sample is especially sensitive to Milky Way dust systematics because of the differences in the samples used for low and high redshift.
2021)  or population fitting  (Popovic et al. 2021a ). Likely, this framework will need to expand for future analyses. There is an alternate approach to obtaining cosmology constraints from SNe that has been gaining traction over the last decade. Bayesian hierarchical models have been developed that utilize bias-corrected observables  (Shariff et al. 2016 ) and that incorporate selection effects directly into the model  (Rubin et al. 2015)  or likelihood  (Hinton et al. 2019). However, unlike BBC in combination with CosmoSIS, these methods have not been validated with large realistic simulations. As noted in Appendix C, we release, as part of this analysis, 10 realistic simulations of the Pantheon+ data set for such validations. While constraints on w should easily improve with upcoming large SN samples, the road to improving constraints on H 0 is more challenging.
In parallel with the aforementioned published works, a solid and widely accessible iconographic database has been created online, representing the collections of museums, libraries and research institutes in many countries around the world (see footnotes 12-16). Thanks to several programmes funded by governments and private individuals, these collections are rich in factual material. Yet, despite the breadth of initiatives digitising works of art, many large photographic collections, even scanned ones, remain in archival repositories with limited or no access. 72 Meanwhile, some private collections have been made visible via Facebook or LiveJournal, 73 albeit on a much smaller scale. These developments reflect the "postcolonial collecting" and "photographic boom" that have swept across the globe, in former colonies and metropoles alike.
Data sources and search strategies Searches were run in MEDLINE, MEDLINE In-Process, MED-LINE E-pub ahead of print (via Ovid), Embase (via Ovid) and The Cochrane Library using both free-text terms and index terms (File S1). Grey literature searches involved handscreening of reference lists from all included studies and HIV treatment guidelines, and searches of clinical trial registries. Additionally, databases of abstracts from selected conferences occurring January 2018-March 2021 were searched (File S1). English-or French-language publications were eligible if they reported on children and/or adolescents living with HIV aged 0-19 years, receiving dolutegravir or raltegravir for firstline or subsequent-line of therapy (Table  1 ). Studies were only included if they presented results stratified by drug, or if at least 80% of the treatment group were on one of the two drugs, and if they reported efficacy or effectiveness and/or safety outcomes through 6 months post-treatment initiation.