dataesr/scientific-paragraphs-categorization huggingface dataset:
5-TM sensors manufactured by Decagon Devices Inc. (now Meter Group) were installed for continuously measuring the soil moisture content at time intervals of 30 minutes. The sensors provide soil moisture measurements at nominal depths of 5 cm (two sensors), 25 cm (one sensor) and 50 cm (one sensor). In this study, only the data collected by the sensors located at 5 cm were used for validation purposes, covering the entire period between 17 March 2017 and 31 August 2018. The reasoning behind choosing two irrigated sites is linked to the data availability. Urgell possesses a limited dataset acquired during field campaigns, whereas the Algerri-Balaguer irrigation district possesses continuous data records. The MOR3 site is representative of a semi-arid climate, with an average temperature of 12 • C in winter and 30 • C in summer.
The mean precipitation recorded is of 217 mm. The site is equipped with two Decagon EC-5 surface sensors at a 5 cm depth and one sensor at a 15 cm depth. The sand percentage is estimated to be between 39% to 57% and clay between 22% to 24% according to ecoclimap and soilgrids, respectively. The dataset used for validation concerns the 5 cm depth sensors only and covers the entire period between 2 April 2016 and 1 March 2017. Table  1  offers a summary concerning information related to the study areas, number of datasets used, in situ acquisition days/periods, and satellite products used. Note that the number of datasets used refers to the total number of samples used for the entire time period in the 1 km validation, detailed in Section 4. 2.
9  show the SMOS SM and the DISPATCH output with respect to in situ measurements for Urgell (2011 and 2015 time periods), Algerri-Balaguer and MOR3. In the same manner as for the spatial validation, the in situ dataset corresponding to Urgell (collected by gravimetric measurements) was aggregated at a 1 km scale by simple linear averaging, for the sampling days. The DISPATCH output presented corresponds to the linear SEE(SM) model with the daily calibration mode, and to the nonlinear SEE(SM) model with the yearly calibration mode. First, results are presented per calibration mode, and then a discussion will be performed.
Various calibration and validation studies of SMOS SM products have reported a negative bias  [68] [69] [70] [71]. The bias in the retrieved SM is inflicted by biases in the brightness temperatures, which could be provoked by the RFI for instance. According to  [72] , a positive bias in the observed brightness temperature would imply a negative bias in the SM products. Since SMOS SM is used when calibrating the SM c parameter, the parameter retrieval is also affected by the negative bias. The downscaled SM data obtained in the nonlinear mode is thus affected by both potential biases in the retrieved SM c values, as well as the negative bias in SMOS data. It is satisfying to observe that a nonlinear representation of SEE, which is widely acknowledged by a number of in situ and modeling studies, provides systematic better results in terms of SEE-based disaggregation.
The key, however, is to develop a calibration strategy adapted to the multi-sensor/multi-resolution data, to their respective uncertainty and to the nature of the model used. Note that the multi-date calibration of each model can be applied over any given period of time, it does not have to be one year only as in our study. However, the multi-date calibration implies a certain latency in the DISPATCH products. In the case of the linear model with a multi-date calibration, the SM p parameter is calibrated as the average of daily SM p values (as previously stated). This implies DISPATCH has to be first run in the daily mode in order to obtain daily SM p values. Similarly, the nonlinear model is calibrated on a muli-date basis using daily SEE LR data. This also implies DISPATCH has to be first run in daily mode in order to obtain these datasets.
shows the SMOS SM and the DISPATCH output with respect to in situ measurements for DOY 223 (year 2015). The error bars present in the plots represent the standard deviation. It is reminded that the in situ dataset corresponding to Urgell (collected by gravimetric measurements) was aggregated at a 1 km scale by simple linear averaging, for the sampling days. The DISPATCH output presented corresponds to the linear SEE(SM) model with the daily calibration mode, and to the nonlinear SEE(SM) model with the yearly calibration mode.
Acknowledgments: This study was supported by the  French Agence Nationale de la Recherche  ( MIXMOD-E  project,  ANR-13-JS06-003-01 ), the  European Commission ERA-NET   COFUND WATERWORKS 2015 Programme  ( Water4Ever  project), and the  European Commission Horizon 2020 Programme for Research and Innovation (H2020)  in the context of the  Marie Sklodowska-Curie Research and Innovation Staff Exchange (RISE) action  ( ACCWA  project, grant agreement no: 823965 ).
Each trip was, therefore, assigned to one of seven fishing gears: (1) surface drifting gillnet, (2) bottom gillnet, (3) purse seine, (4) circling gillnet for sardines, (5) circling gillnet for mullets, (6) longline and (7) handline. The average deployment duration of each GPS unit ranged between 1 and 10 days before needing to be recharged and/or replaced with a new unit. The number of individual fishing trips recorded for each gear ranged between 8 and 32 (mean±SD number of trips recorded per gear: 14 ± 8; n = 7 gears). Once each GPS unit was retrieved, the recorded data were downloaded and processed to remove possible erroneous location data, which included: (1) removing pre-and post-deployment locations associated with travel to and from the landing sites; (2) removing locations that were within 3 km of landing sites as these are associated with transiting and are areas where fishing is prohibited; and (3) applying a speed filter to remove locations with a speed > 50 km.
h -. This threshold was based on maximal speed of 40 HP engines typically used by small-scale fisheries. In a time series of locations, time interval standardized data are a prerequisite for Hidden Markov Models, therefore, GPS data for each fishing trip were standardized to 5-second intervals to avoid time lag and to eliminate missing locations from poor satellite coverage using speed-based linear interpolation. After processing, the resulting dataset comprised of 99 tracks, and 818,747 vessel locations (longitude and latitude decimal degrees, WGS84). To gain an insight into the general operating behavior of smallscale fisheries in the region, information on total trip duration and total distance were calculated and summarized for each individual gear type. Data were processed in R using adehabi-tatLT, rgdal, geosphere packages in R  [54, 55].
To choose the number of states, the distribution of speeds for each gear was observed and integrated with existing knowledge about fishing techniques. In HMMs, seed values for the model parameters need to be specified. In this case, data from on-board observers were used to provide initial parameter values. Step lengths followed a gamma distribution, and angles a Von Mises distribution  [64]. For each gear type, mean and standard deviation of step lengths and concentration of angles were calculated for each state (fishing and non-fishing), to determine initial parameter values. HMM fitting. The R package MoveHMM was used to determine fishing and non-fishing states from the GPS tracking data  [57, 65]. A Hidden Markov Model was fitted for each gear type, using the forward algorithm  [65]. To determine the most probable state sequence, a Viterbi algorithm was used.
Method performance evaluation Confusion matrices were used to determine the performance of the HMM and visual interpretation approaches. Using the confusion matrix, several performance measures were calculated, these included: (1) sensitivity, which is the probability that a test will indicate 'fishing' among those which were actually fishing; (2) specificity which is the probability that a test will indicate 'non-fishing' among those which were actually not fishing; (3) fishing prediction which indicates the probability of true fishing positions among fishing positions detected by the model; and (4) non-fishing prediction which indicates the probability of true non-fishing positions among non-fishing positions detected by the model  [66]. For trips with observers on-board (n = 7), observed data on the occurrence of fishing activity were compared to fishing detected during expert interpretation and from the HMM process.
Visual interpretation and HMM approaches were also compared. Since for the rest of tracks (n = 99), fishing trips did not have an observer on-board, the visual interpretation was used as the truth against which to assess the accuracy of HMM. To test the utility of this approach on datasets with different temporal sampling resolutions, the HMM was fitted to data regularized at different time intervals (i. e. 10, 20, 30, 40, 50 and 60 seconds, and each of 2 to 10 minutes at 60-second intervals). Ten minutes was the maximum time step tested as it was typically the shortest duration of a fishing operation, especially for handlines. To assess the most efficient method for detecting fishing activity within GPS tracking data (i. e. minimum analytical time), three time-based metrics were calculated: (1) the mean duration of a fishing trip hosting an on-board observer, (2) the mean duration required to visually inspect tracking data during the expert interpretation process, and (3) the mean duration required to complete the execution of the HMM.
In those cases, transition between two fishing events could be classified as fishing by visual interpretation and by HMM. Accuracy of the HMM became weaker when applied to a larger dataset but it is possible that the accuracy of the visual interpretation decreases also. To assess this tendency, more data from on-board observers would be necessary. Those data would also allow to have more precise model parameters but in a context of working with fishing communities, gaining trust takes time and observers are not always accepted. This is why the sample size was low and this should be addressed in future studies when trust is well established. Lower model performance accuracy occurred for fishing gears where fishermen let the gear soak longer in the water. In these cases, time was spent drifting and sometimes fishers left their gear in place and moved the boat to another area.
This work was supported by  US Fish and Wildlife Service ,  AFR-1427 / F14AP00555 , https:// www. fws. gov/. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. BJG, KM, and MJW were supported by the  Darwin Initiative  (Projects  17-005/20-009/23-011/26-014 ) through funding from the  Department for Environment, Food  and Rural Affairs in the UK. SB was supported by the  LMI TAPIOCA, program CAPES/COFECUB  ( 88881. 142689/2017-01 )and  EU H2020   TRIATLAS  project under Grant Agreement  817578. FL was supported by  Arc Emeraude Project  ( ANPN/AFD ).
Bibliographic search Structured and semistructured searches were conducted using major scientific databases (namely, Scopus, Web of Knowledge, and Google Scholar) along with appropriate cross-referencing to obtain supporting literature. Additional searches were based on the authors' own knowledge of po-tentially relevant work and their experience with constructed Technosols. Structured searches were carried out with the following keywords (title, abstract, and keywords): constructed Technosols, engineered soils, Anthroposols, green space, parks, green infrastructure, green roof, storm water management, street trees, ecosystem services, densification, abandoned land, food security, soil functions, biodiversity, urban farming, recycling, landfill, and waste management. The literature considered for the review was published in English, French, and Russian.
Acknowledgements. The authors are thankful to  Landry Collet  for allowing the use of his pictures for publication (all photos in this paper are from  Landry Collet , unless stated otherwise). Financial support. This research has been supported by the  RUDN University  's "5-100 "  project (grant no. 19-77-300-12 ). Review statement. This paper was edited by  Cristine Carole Muggler  and reviewed by two anonymous referees.
To estimate the tolerated erosion for each plan the universal soil loss equation (USLE) is used  (Wischmeier and Smith 1960) , its revised version (RUSLE)  (Renard et al. 1991 ) and the open software Erosion 6. 0. 20 developed in Uruguay (García  Préchac et al. 2009). In addition, the policy stipulates rotation and other management practices must be adopted. Before the SUMP became mandatory in 2013, the policy went through a pilot phase, between 2010-2012, engaging a small group of volunteer farmers who were owners or tenants of croplands. During the pilot phase, several activities were promoted, including more than 100 workshops with farmers and agronomists to discuss the new instrument. Two types of workshops were held. One type corresponded to instances of dissemination, training, and discussion about the implementation of SUMP with producers.
The construction of the online platform to submit the plans was also an experimentation process which lasted five years  (2013) (2014) (2015) (2016) (2017). In 2013, a 'very precarious' platform was available, as an agronomist involved in the process states. In this platform, the certified agronomist uploaded the general information of the agricultural land, a file with the geographical information (defined by Google Earth or other similar programs) and an estimation of the soil erosion calculated by the open software (Erosion 6. 0. 20). During 2014 and 2015, the RENARE worked with the Ministry's IT technicians and a software development company to make the online platform more precise with the addition of a geographical viewer that enabled to draw the agricultural lands in real-time. The improved version of the software posed a relevant problem to migrate information from the original tool to the new one.
Then, continuous tests were done with RENARE technicians until it became operational. The final version of the software was approved by the Technical Committee. With the new platform, the plans were submitted online using Erosion 6. 0 (free software) and were controlled and monitored through satellite images. In this way, the computer system and satellite images to analyze the basic information of the submitted plans allowing the government to oversee their implementation. In particular, the system could identify locations at a higher risk of erosion as well as locate farmers who were not complying with the crop rotation plans that they had submitted (technician interview). In synthesis, the concept of experimentation from a public policy perspective differs from the traditional positivist approach, and it refers essentially to a process of learning by doing in which new approaches are discussed with networks of actors to deal with the different aspects concerning challenging public problems.
As an academic underlined, 'The studies on soil erosion, started in the 60s […] we obtained a database in three soils and geographical places with different topographies that allowed to validate the USLE/RUSLE model. Likewise, a software based on the model to be applied throughout the country was developed to teach in Faculty (its current version is Erosion 6. 0) […] Subsequently, further research on soil erosion measurement with interinstitutional agreements was continued'. The implementation of SUMP allowed the capitalization of decades of research in the country. Thus, the process of policy design implied generative experimentation that sought to create a solution based on accumulated experience and knowledge. At the same time, in contrast to previous public efforts to deal with erosion, such as the CIDE-experience, the SUMP was less technocratic and top-down, while opening up for broad participation and knowledge claims from different traditions.
Patient Sample CKDopps is an ongoing prospective cohort study of stage 3-5 ND-CKD patients treated in nephrologist-led CKD clinics in Brazil, France, Germany, Japan, and the United States. CKDopps sites were randomly selected from CKD clinics after stratification by region. CKDopps study design details and objectives have previously been published. 18 CKDopps was approved by national and/or local ethics committees, as required by local ethics regulations, with informed patient consent obtained, as required. Our analyses included Brazil and US patients (2014-2018). Data from Japan and Germany were unavailable at time of analysis; French sites used a different physical activity questionnaire. From the initial sample of 2,465 enrolled patients, different subsets were included in each analysis (Figure  S1 ).
Outcome Definitions Baseline was defined as date of Hgb measure or RAPA response. Outcomes were according to the following: Health-related Quality of Life Self-reported HRQOL was assessed via the KDQOL-36 questionnaire. 16, 17 Items were summed to yield burdens and effects of disease, mental component summary (MCS), and PCS scores. Data to calculate symptoms of disease score were not available for all country cohorts. We also calculated scores for eight MCS and PCS subdomains: emotional role, emotional well-being, energy, general health, pain, physical function, physical role, and social functioning. We treated each summary score as a continuous measure, with higher scores indicating higher HRQOL across the possible range of 0-100.
Multiple imputation, implemented by IVEware,  23  was used in all analyses to impute missing covariate values, and in the minority of cases when only some survey questions for calculating KDQOL-36 and RAPA scores were answered. Twenty complete data sets were imputed, all analyses were performed with each data set, and results were combined using Rubin's rules. 24 ll analyses used SAS software, version 9. 4 (SAS institute, Cary, NC). Confidence intervals (CIs) are reported with 95% confidence level.
ESA and IV iron usage is reported both in combination and overall. ESA and any iron use refers to usage of ESA with either IV or oral iron, requiring that data on at least one of the two forms of iron is not missing. Very high proteinuria was defined as albumin-to-creatinine ratio $300 mg/g or albumin $300 mg/day. The comorbidity score was calculated per patient as the number of comorbidities out of the following 10: coronary heart disease, hypertension, other cardiovascular disease, cerebrovascular disease, peripheral vascular disease, recurrent cellulitis/gangrene, neurologic disease, psychiatric disorder, gastrointestinal bleeding, and cancer. *0%-4% missing. †18% missing. ‡27%-34% missing. §62%-69% missing. k 10%-11% missing. In adjusted analyses, more severe anemia was most strongly associated with poorer HRQOL summary scores for the burden and effects of kidney disease (Figure  2 ).
Our study patients had stage 3-5 ND-CKD and treated in nephrologist-run clinics, with 46% having Hgb. 12 g/dL, of which only 4% were prescribed an ESA and 1% prescribed IV iron. None would have been eligible for TREAT or CHOIR trials. Thus, our real-world findings indicating strong associations of particular HRQOL domains with higher Hgb levels may be more representative of the broader ND-CKD patient population. In addition, our analyses extended beyond purely physical-and energy-related domains. Some of the strongest associations were with emotional role and the burden and effects of kidney disease. ] [29] [30] [31] [32]  Very limited information exists regarding the connection between anemia and physical functioning in ND-CKD patients. 7, 8, 33 In response, we collected data on patientreported physical activity levels via the RAPA instrument.
Practical Application Hgb levels, as an indicator of anemia, have been inconsistently associated with HRQOL and physical activity in persons with moderate-to-advanced CKD in observational studies and RCTs. In the present study, we analyzed data from 2,121 persons having stage 3-5 CKD (not on dialysis) being treated at 28 U. S. and 15 Brazilian nephrologist-run CKD clinics. Our findings showed that Hgb levels. 12 g/ dL were associated with slower progression of CKD, greater survival, higher odds of being physically active, and better HRQOL (for HRQOL domains of general health, emotional role, physical role, and burden/effects of kidney disease). However, additionally, an important finding of this work is that even moderate anemia (Hgb levels from 10 to 12 g/dL) was associated with poorer HRQOL, rate of CKD progression, and mortality.
Acknowledgments Janet Leslie , medical technical writer, with Arbor Research Collaborative for Health, assisted in revising the presentation of the researchers' results and finalizing the manuscript. Heather Van Doren , senior medical editor with Arbor Research Collaborative for Health, provided editorial assistance on this manuscript. Financial Disclosure:  Coauthors Heleen van Haalen  and  Glen James  are employees of  AstraZeneca. The authors declare that they have no relevant financial interests. Support: This work, produced by CKDopps as part of the  Dialysis Outcomes and Practice Patterns Study (DOPPS) Program , has been supported by specific funding from  AstraZeneca. To see all funding for the DOPPS Program, please visit dopps. org. All support is provided without restrictions on publications.
[69]. Twenty male gammarids and 20 leaf discs (20 mm diameter) were enclosed in perforated cylinders (four replicates per condition), which were then placed in the buckets during Cd exposure. After 7 days, leaf discs were collected and numerically scanned to estimate leaf consumption. The procedure was slightly modified for the F1 generation (5 months old) to preserve the stock of spawners dedicated to pursuing the experiment. Three replicates of 4 males/4 leaf discs were considered per condition. The feeding rate was not monitored for the F2 and F3 generations. Growth: Gammarid juveniles were photographed under a binocular light microscope at a three-week interval. Then, photographs were analyzed using the SigmaScan®Pro v5. 0 software (Systat Software, San Jose, CA, USA). The body size corresponds to the dorsal length from the start of the prosoma to the end of the metasoma.
For the number of oocytes, corrective factors established from Geffard et al. [35]  were applied to consider the influence of molt stage (e. g. , 30% reduction between C1 and C2 stages). Since the number of oocytes and embryos depends on female body size, standardization has been performed as proposed in Geffard et al. [35]  by dividing the total count of oocytes by female body size and those of embryos by female body size minus size at puberty (5 mm). Regarding the analysis of the secondary follicle surface, females without oocytes were discarded from the dataset, and females at B and C1 molt stages were not considered, since secondary vitellogenesis is not initiated in these stages. Corrective factors from the study of Geffard et al. [35]  were applied to make it possible to compare follicle surface measurements between females at the C2, D1, and D2 molt stages (using C2 as reference).