dataesr/scientific-paragraphs-categorization huggingface dataset:
The R rs spectrum calculated for each station is the median of at least 25 over-water observations from this series. These spectra were corrected for sun-glint following the similarity spectrum-based approach by  Ruddick et al. (2006). They were the first and, so far, the only available in-situ remote sensing dataset collected for the NHR estuary region. A blue (488 nm) to red (667 nm) R rs band ratio (i. e. , MODIS bands 10 and 13) was used to formulate an empirical relationship with in-situ CDOM absorption at 412 nm using a non-linear regression approach  (Doxaran et al. , 2005; Campanelli et al. , 2017) : a inÀsitu cdom ð412Þ ¼ 2:21 R inÀsitu rs ð488Þ R inÀsitu rs ð667Þ ! À1:24 ð4Þ Following  Nechad et al. (2010) , non-linear empirical relationships were established for TSS retrieval using a single red band of R rs 667 nm: TSS inÀsitu ¼ 5:49 exp Â 53:31 R inÀsitu rs ð667Þ Ã ð5Þ The fits of the data to these two relationships are shown in Figure  3a  and b, respectively.
These simple empirical relationships (Equations 4 and 5) were used to retrieve CDOM and TSS from geolocated Moderate Resolution Imaging Spectroradiometer (MODIS) images (on NASA's Earth Observing System Aqua and Terra satellites). Level 1A radiance to R rs conversion was carried out using the Management Unit of the North Sea Mathematical Models (MUMM) atmospheric correction module  (Ruddick et al. , 2000). This conversion was performed using the l2-gen processor of NASA's SeaDAS image processing platform. The cloud-albedo threshold of 0. 40 was used in the l2-gen processor instead of the default 0. 20 to avoid flagging of highly reflective turbid waters of the NHR estuary as cloud pixels  (Matsuoka et al. , 2016). A match-up analysis, centered on station locations, was performed to ascertain the retrieval efficacy of the empirical relationships, resulting in 15 match-up points with a <2-hour difference between in-situ sampling and the satellite overpasses.
For example, in Figure  4c , the threshold line crosses the Q 0. 50 values in the section located approximately 110 km from the river mouth. Thus, at 110 km from the NHR mouth, 50% of CDOM exceeds the threshold (i. e. , 0:50 0:95 or 0. 53 marine water fraction). Therefore, we calculate a river-influenced water fraction of 1 À 0:50 0:95 or 0. 53 at 110 km. Similar calculations were performed for all semi-circular sections within the CDOM and TSS imagery to model the dispersion of river CDOM/TSS-influenced waters. These imageries corresponded to three daily discharge conditions: low discharge (LD; 1500-4200 m 3 s À1 ), mid discharge (MD; >4200-6500 m 3 s À1 ), high discharge (HD; >6500-9000 m 3 s À1 ), and tidal stages (flood and ebb). The NHR discharge data were obtained from Manitoba Hydro at the Long Spruce generating station.
Acknowledgments This study is a contribution to the  Arctic Science Partnership (ASP). The authors thank  Manitoba Hydro  for the 2006 Nelson estuary field campaign and  Mats Granskog  for collection and processing of CDOM samples. They thank the continued support of various members of the  Centre for Earth Observation Science, University of Manitoba , where this research was conducted. We also thank anonymous reviewers, associate editor, and editor-in-chief whose comments contributed to changes which improved the quality of this manuscript. As a special mention the authors thank the late  David Barber  for his inspirational support for this research.
Data Collection Methods to Identify and Characterize Livelihood and Small-scale Agricultural Activities An initial social assessment of the study communities, livelihoods and small-scale agriculture was documented through a literature review and focus group discussions with community members. This provided a basic picture of the livelihood patterns practised by communities in the Great Letaba River region and an indication of their relationship with the river, their key priorities and activities. Outcomes from the study provided the context of the area which was used later for a detailed evaluation of the sustainability of agriculture in the catchment. The baseline survey was conducted through focus group discussions and interviews with small-scale farmers in the local communities. These group discussions and interviews identified ecosystem services available from the Great Letaba River including small-scale agriculture, general livelihoods and wateruse demands through the years.
Target Population and Sampling Techniques Before any of the above-mentioned data collection activities were carried out, a 'familiarization' of the catchment area was undertaken. This exercise presented opportunities to engage with members of the catchment community, which comprised mainly small-scale farmers. It was meant to get conversations started so that the researchers could get to know the potential participants and identify the target population. The target population of the study were mainly small-scale farmers from the local communities who are water users of the Great Letaba River. The selection of participants was guided by  Etikan et al. (2016)  who recommend purposive sampling in case studies to allow in-depth analysis of the phenomenon under study. Participants were recruited randomly at first; thereafter, additional participants were recruited through direct recommendations by other participants, in a method called snowball sampling.
As per the criteria for selection, participants had to be small-scale farmers from the community who live in the Great Letaba River area and use the river waters. After the participants' consent was obtained, data collection was carried out during May and June 2021. The target participants were engaged at different times using different research methods as shown in Table  1. The objective of the fieldwork was to determine subsistence use of river water and livelihoods related to flows in the Great Letaba River. This included groundwater use where it can be possibly associated with the flow of the river. This was determined through focus group discussions, interviews with community members and field visits over two months. The data collected were validated through field tracking (where the researcher is accompanied by a local key informant to identify the key features mentioned by the participants with the assistance of aerial photos).
We gathered ideas and used break-out rooms and plenary sessions to draw the impact pathways together using simple collaborative online drawing tools. While this mode of operation proved adequate for generating the model presented here, a more intensive in-person exercise may have generated richer results, e. g. , on potential implications for upstream water users. The developed conceptual model was used to generate an R function that takes in the variables provided in the input table and produces a model output. To build these simulations, we used functions from the decisionSupport  (Luedeling et al. 2021) , dplyr  (Wickham et al. 2022) , nasapower  (Sparks 2018) , patchwork  (Pedersen 2020) , tidyverse  (Wickham 2022)  and evapotranspiration  (Guo et al. 2022 ) libraries in the R programming language (R Core Team 2021).
Simulations To simulate agricultural outcomes for the three e-flow scenarios, functions of the decisionSupport package for the programming language R  (Luedeling et al. 2021)  were used. This package provides a function to compute plausible distributions of model outcomes through a Monte Carlo simulation. In a Monte Carlo simulation, a large number of random values are drawn for each input variable according to user-defined probability distributions. The model is then run for each combination of values, with the resulting population of model results assumed to represent the plausible distribution of outcomes. In contrast to the precise values produced by fully deterministic calculations (which do not consider uncertainties), these distributions reflect the limited predictability of the real world and the limitations imposed on the modeling process by the imperfections of our knowledge about all model input parameters.
The outputs of a Monte Carlo simulation are useful for bracketing the plausible range of system outcomes. They also offer an opportunity for further explorations, especially when the simulation is run with uncertainty estimates that approximate the current state of knowledge. In such cases, the outcome distribution can be related to variation in all input parameters to identify important uncertainties, which determine the magnitude of expected outcomes. Additional precision on system outcomes may then be gained by measurements of highly influential variables. The decisionSupport package implements such analysis in the form of Partial Least Squares (PLS) regression, whose 'Variable Importance in Projection' (VIP) scores identify important variables. Wherever a model simulates a decision between alternative options, e.
In decisionSupport, this calculation is implemented through the 'Expected Value of Perfect Information' (EVPI), which is based on the (usually unattainable) state of perfect information on a particular variable. The EVPI can be understood as the maximum amount a rational decisionmaker should be prepared to pay to completely eliminate uncertainty on an uncertain variable. In most decision simulations, few variables, if any, are so influential that additional information on them can change the decision recommendation that results from the simulation. Where such high-value variables are identified, the respective uncertainties emerge as promising entry points for decision-supporting research. All major elements of decision analysis are provided in the decisionSupport package  (Luedeling et al.
This is only about one-tenth of the estimated e-flow requirement, a small addition that would make a substantial difference to livelihoods. The livelihoods e-flow SUPPL scenario has the potential to generate considerable benefits for smallholder farmers, who would see their irrigation water shortages alleviated. Whether or not this scenario is realistic depends on the availability of upstream water resources, as well as the possibility to restrict some of the current uses of water upstream. Annex 1. Model Scripts. This script was generated using the rmarkdown  (Allaire et al. 2021)  and knitr  (Xie 2022)  packages in the R programming language (R Core Team 2021). The following script contains the basic model we used to run the Monte Carlo analysis:.
# We compute crop water need based on ET 0 based on the Hargreaves Samani Equation, as implemented in theEvapotranspiration package. ET o is reference evapotranspiration mm/ha for the model simulations. # Input temperature data comes from the NASAPOWER dataset (accessed through the nasapower package). # The scenario data are based on scenarios that represent conditions during real years in the past. # To get from ET 0 to crop water use, we need to multiply ET o with a crop coefficient (kc), which is estimated for each month. limpopo_decision_function <-function(x, varnames){ # generating boundary conditions for the simulation run # simulate how much rainwater is available rainfall<-sapply(1:12,function(x) eval(parse(text=paste0("prec_",x)))) effective_rainfall<-sapply(rainfall,function(x) min(x,effprec_high)) effective_rainfall<-sapply(effective_rainfall,function(x) max(x,effprec_low)).
Age dating The radiochronology of the upper core segment (0-20 cm) of the subcore Tehua XII E03a was estimated through the 210 Pb age dating method at 1 cm resolution (Table  S1 ). 210 Pb activities were determined at the laboratory of Isotopic Geochemistry and Geochronology at UNAM (Mexico) through high-resolution gamma ray spectrometry (HPGe well detector, Ortec-Ametek) as described by  Díaz-Asencio et al. (2020). For validation, 239,240Pu  was determined in selected sediment samples by alpha spectrometry (Ortec-Ametek Alpha Spectrometry system)  (Ruiz-Fernández et al. , 2014). Data quality was assessed through the analysis of the reference material IAEA-300 (Radionuclides in Baltic Sea sediment) and the results were within the reported range of recommended values. Radiocarbon ages were determined by accelerator mass spectrometry (AMS), at the National Platform LMC14, France, on five bulk sediment samples (4.
Age-depth model The 210 Pb chronology was established a) with the Constant Flux model (Sanchez-Cabeza and Ruiz-Fernández, 2012), with uncertainties estimated through a Monte Carlo method with 10 6 simulations  (Sanchez-Cabeza et al. , 2014) , and b) with a bayesian method (Aquino-López et al. , 2018) by using the R package rplum  (Blaauw et al. , 2021) , which has been successfully used in contrasting aquatic environments, including marine sediment cores (Aquino-López et al. , 2020). Both chronologies were almost identical and were successfully validated with the 239,240Pu onset, which is a robust age-dating marker in regions far from nuclear testing grounds (Sanchez-Cabeza et al. , 2021) (Figure  2 ). Following common practice, the radiocarbon ages were calibrated with the Marine20 curve  (Heaton et al.
The 210 Pb-derived ages were expressed as BP (before present; age BP = 1950 -calendar age) and produced 10 6 simulations of the 210 Pb age BP (pb210. bp) and the sample radiocarbon age (c14) following normal distributions. Simulated 210 Pb BP ages younger than 1950 were discarded as they could not be calibrated with the MARINE20 curve. Each pb210. bp simulation was reverse-calibrated with the MARINE20 curve (pb210. c14) by using the calBP. 14C function of the IntCal package  (Blaauw, 2022). Then, the ΔR simulations were determined as the difference between the sample radiocarbon age simulations (c14) and the 210 Pb reverse-calibrated age simulations (pb210. c14) as ∆ = -R c pb c 14 210 14. Finally, ΔR and its uncertainty were determined as the mean and standard deviation of all simulations, respectively (ΔR = 247 ± 30 years, 1 sigma).
The integrated 210 Pb-14 C age-model was produced by a bayesian approach  (Blaauw and Christen, 2011)  with the rbacon R package  (Blaauw et al. , 2022). We used all 210 Pb-derived ages and the three radiocarbon 14 C ages beyond the validated 210 Pb chronology (sections below 20. 0 cm), with the MARINE20 curve and the calculated ΔR. The integrated age-model showed a satisfactory agreement between 210 Pb and 14 C ages, and a common and smooth trend (Figure  3 ). The integrated 210 Pb and 14 C age model indicated that the base of the core reached ~1500 CE (Table  S4 ; Figure  3 ). The average sediment accumulation rate from ~1907 to ~2014 CE was 0. 14 ± 0. 07 cm/year; whereas for the older sediment, the sedimentation rate was of 0. 08 ± 0. 06 cm/year (Tables  S1 to S2 ).
Acknowledgements We thank the crew of the  R/V "El Puma"  for their assistance during the TEHUA XII expedition and core collection. Libia Hascibe Pérez Bernal  performed the radiometric analysis for 210 Pb dating. Laura Elena Gómez-Lizárraga  provided technical aid with the SEM images at the  ICML, UNAM. We are also indebted to anonymous reviewers for their valuable suggestions to improve this paper.
Funding The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: Financial support for the development of this project and publication costs was provided by the Instituto de Ciencias del Mar y Limnología, UNAM. Support for field sampling was provided by the UNAM through oceanographic mission TEHUA XII. The first author (LAR) thanks the Graduate Program in Marine Science and Limnology at UNAM, Mexico, and the financial support provided by the National Council of Science and Technology (CONA-CYT) for the doctoral scholarship (Grant number: 556646), as well as the Institut de Recherche pour le Développement (IRD) for the fellowship grant for an academic stay at the Laboratory of Oceanography and Climate (LOCEAN), IRD France-Nord.
Electrochemical characterization Electrochemical performance towards oxygen evolution was measured in a custom made photoelectrochemical cell with a three-electrode system. The light source was a Xenon lamp calibrated to an intensity of 100 mW. cm -2. The thin films were used as working electrode, with a Pt foil as the counter electrode and Ag/AgCl (KCl 3 M, E = 0. 210 V vs NHE) as reference. The electrolyte was a 0. 1 M phosphate buffer solution at pH 7. (Photo)electrochemical measurements were made with an Autolab PGSTAT302N potentiostat coupled with Nova software. Voltammetries were performed at 10 mV/s. Electrochemical impedance spectroscopy data was gathered between 10000 and 0. 01 Hz using a 10 mV amplitude perturbation. Data were fit using either Nova software or impedance. py Python package.
Acknowledgements This work was financially supported by  Agencia I+D+i   PICT 2017-4651 ,  PICT 2018-01407 ,  PICT 2018-04236 ,  PDTS RD-0367 16  and UNSAM. P. V. and L. N. M. thank  CONICET  for the fellowship grant. F. H. thanks  Agencia  for his fellowship grant. M. P. K. thanks Y-TEC. F. A. V. and G. J. A. A. S. I. are members of  CONICET.
Data acquisition was performed in time-resolved analysis mode, with 30 s background measurement before each analysis. For trace elements, primary standard SRM-NIST 610 was analyzed every 10 samples, secondary standard SRM-NIST 612 every 15 samples to assess the analytic accuracy during the session. Each analysis comprised a series of 34 elements for a total sweep time of 0. 626 s. Data are reported in Table  S2.
01 pg ±50% is used for all data reduction. All analyses were calculated using 1. 15 pg of laboratory blank and the remaining common Pb was attributed to initial common Pb. Data reduction and uncertainty propagation were done using Tripoli, YourLab and Isoplot software/excel  (Bowring et al. , 2011; Ludwig, 1990; McLean et al. , 2011; Schmitz and Schoene, 2007). Isochron calculations were performed using IsoplotR  (Vermeesch, 2018)  with all uncertainties reported in 95% confidence level including overdispersion (without systematic uncertainties associated with the tracer calibration and decay constants). All the results are reported in Table  S5.
7a ). With the available data, it can be concluded that perovskite in ophicarbonates and calcite veins, like other metamorphic perovskites, is relatively homogeneous in trace element composition within each sample. From the limited dataset, it is however evident that metamorphic perovskite displays significant variation between ultramafic and mafic lithologies, particularly in REE. The samples investigated in this study and data from literature pertain to perovskite that equilibrated at different metamorphic conditions, spanning from greenschist-facies to ultra-highpressure conditions (Figs. 7, 8 ). However, the observed variability is not correlated with the metamorphic degree. The variability in trace element composition of perovskite is rather related to the protolith and the partitioning with co-existing phases, i.
Acknowledgment This work was supported by the  Swiss National Science Foundation  grants  200021_191959  to  D. Rubatto  and  206021_170722  to  D. Rubatto  and  T. Pettke. This work was also supported by the  European Research Council (ERC)  under the European Union's  Horizon 2020 research and innovation program  (Grant agreement No. 864045  to  A. Vitale Brovarone ). The project has benefited from the expertise and the facilities of the  BiGeA Raman Lab. , University of Bologna. The authors thank  Balz Kamber  for editorial handling, as well as  Larry Heaman  and one anonymous reviewer for their constructive comments. We wish to thank  B. Hofmann  for providing access to the mineral collection of the  Natural History Museum of Bern. G. Pearson  is thanked for discussion on IR perovskite.
, 1998). In purposive sampling, participants are selected to meet a specific set of criteria. Gill, et al. , (2008)  categorised three different types of interviews which are: structured, semistructured and unstructured. Primary data for this research was obtained through 31 semistructured interviews conducted face-to-face. This type of interview allows the researcher to discover or elaborate important information that at the beginning had not been taken into consideration. There is much more information which can be attained, besides from the actual vocal response towards the questions from social calls such as body language  (Opdenakker, 2006). However, during this process the emphasis is not towards the interviewee's personal emotions but their expert knowledge and industry experience. The interviews were recorded using smartphone 10 using the app voice memos.
Once the interviews were undertaken each of the interviews were transcribed verbatim using Microsoft Word. This was supplemented with field notes as appropriate. According to  Saunders et al. , (2019)  saturation is a term used to describe the point when no new insights or range of ideas are generated through adding more data. Creswell and Creswell (2018)  explained that a theoretical sampling process usually involves 20-30 interviews before data is saturated. In this study, data was collected until new aspects of the issues women are facing in construction sector were revealed. Actual saturation of data occurred before the 30 interviews as information provided by the interviewees became repetitive and no new information was being identified. The interviews lasted between 30 and 80 minutes.
Thematic analysis is a method used to identify, analyse, organise, and describe a set of data that has been collected and was therefore used in this study. This method allowed a flexible structure because it can be modified depending on the nature of the study, thus obtaining an indepth description of the data  (Nowell, et al. , 2017). The transcribed interview data was exported to NVIVO software programme to review the information. This allowed to develop child and parent nodes which them formed themes for this study. Table  1 , displays the 31 interviewees details, explaining that all interviewees were women, aged between 25-34, with varying ethnicities, construction disciplines and levels of education. The diverse backgrounds are essential to this study, as this allows a wide range of experiences and knowledge to evidence the discussions and conclusion of this research study.
Acknowledgments This work is performed in the framework of the research programs DATSHA supported by the  French national program LEFE (Les Enveloppes Fluides et Environnement) and Foundation Air Liquide , and was also supported by  LISA , UPC ,  UPEC ,  UMR CNRS 7583  via its internal project call. The analyses presented were performed with the instruments of the  PRAMMICS platform OSU-EFLUVE UMS 3563. The authors would like to acknowledge  SIRTA  for providing the ceilometer data used in this study.
Application to Finite Element generated data Since the present work focuses on the verification of the methodology, no experimental data was processed. The identification procedure with the Laplace Virtual Fields Method was applied to data generated via Finite Element (FE) simulations. The implicit solver Z-set developed at ONERA and Mines Paris Tech was used  [40]. An uniaxial tensile test was simulated on a double notched specimen geometry to generate heterogeneous kinematic fields. The isotropic linear viscoelastic constitutive relationship given by Equation (  2 ) was used within a 2D plane stress hypothesis. The integration of this constitutive relationship was performed using a temporal implicit scheme. The time dependant moduli K (t ) and G(t ) were described with a Prony series implementation available in the Z-mat material library.