dataesr/scientific-paragraphs-categorization huggingface dataset:
Human Health Analyses Clinical Health and Anthropometric Data Analyses Anthropometric data were analyzed using standard WHO cutoffs to assess nutritional status. Children under 5 were assessed as underweight (weight-for-age z-score <-2), being stunted [height-for-age z-score (HAZ) <-2], wasted (weight-for-height z-score <-2) and overweight (weight-for-height z-score > 2) based on the WHO Child Growth Standards  (52). Mid-upper arm circumference (MUAC) was used to assess severe acute malnutrition in children based on a MUAC <11. 5 cm in children under 5, and moderate acute malnutrition based for a MUAC ≥ 11. 5 cm and <12. 5 cm. For children <24 months, a headcircumference-for-age z-score <-2 was classified as a case of microcephaly. In children ages 5-19, overweight was classified based on a body-mass-index-for-age z-score (BMIZ) > 1, and obesity was classified as a BMIZ > 2.
Ecological Analysis The second module featured marine ecological research on the health and status of the coral reef system. To examine how coral reef health influences human health, the ecological research module focused not only on species with key functional roles, but also on species and ecosystems significant to I-Kiribati fisheries, food security, and cultural importance. Ten ecological indicators (EI) across multiple dimensions were used to assess ecosystem health, capture natural variation, and incorporate interactions between species and the environment. Variability in the structural composition and geomorphology of coral reef atolls, as described above, constitutes distinct ecosystems, or habitats, with different species compositions and oceanographic conditions. The back reef, where SIQ infaunal survey data was collected, hosts a different, but important, assemblage of species than those found on the atoll's lagoon or fore reefs.
Thus, analysis was focused on the status of different species assemblages across all habitat types. We use (EI 1) abundance, (EI 2) richness, and (EI 3) size structure data of frequently harvested and functionally important fish and invertebrate species from the community surveys as primary ecological indicators. For example, species such as the humpback red snapper Lutjanus gibbus or green humphead parrotfish Bolbometopon muricatum, respectively, can offer insights into the relative health of the lagoon reefs or fore reefs when using these three indicators. With the community survey data, we then assigned fishes to (EI 4) broad trophic categories and converted counts and total lengths to (EI 5) biomass density using trophic classifications and length-weight conversion compiled by the Ecosystem Science Division of NOAA's Pacific Islands Fisheries Science Center from FishBase  (80).
While the community, manta tow, and infaunal surveys provided a way to evaluate the frequently harvested and functionally important fish and invertebrate species, the benthic surveys also contributed supplementary ecological indicators of coral reef health. The transition from a coral-dominated reef to a degraded, macroalgal-dominated reef is the most common way to assess coral reef functional health when comprehensive benthic data is available  (83). Thus, the (EI 8) percent substrate cover and proportion of scleractinian framework-building corals vs. macroalgae and/or turf was used to determine the level of degradation across sites and islands. Similar to EI 6 for fishes, we then assessed the (EI 9) functional diversity of the coral assemblage using the life history categories proposed in Darling et al.
Oceanographic Analysis In addition to the ten ecological indicators, oceanographic indicators also can modify ecosystem health. Eight biophysical drivers were considered to evaluate the quality of the ocean water and further describe current reef conditions. We measured (OI 1) salinity and (OI 2) conductivity to indicate the concentrations of salt ions and dissolved solids in water, respectively. Temperature data (OI 3), both current and historic, was used to monitor current conditions, which can be a precursor to harmful algal blooms and coral reef degradation. For in situ data, (OI 4) chlorophyll A was used to measure primary productivity as an indicator of runoff. We coupled the chlorophyll A data with nitrogen, alkalinity (KH), calcium and phosphorus within the analysis to triangulate water quality due to the biochemical properties of ocean water (unlike freshwater tests).
By using the 8 oceanographic measurements and historical data on climate impacts (i. e. , NOAA Coral Reef Watch: degree heating weeks), we can produce maps of spatially explicit climatic influences. Together, these variables provide a baseline environmental gradient to evaluate anthropogenic impacts on coral reefs. Eventually, we will evaluate these biophysical drivers in conjunction with heavy metals, diatom species identification, and ciguatoxin analysis to examine associations with human health.
Total Mercury Analysis Ground samples were analyzed at Harvard University for total mercury concentration (T-Hg) on a direct mercury analyzer (DMA) (NIC MA-3000 Mercury Analyzer and MA3 Win software) following US EPA Method 7473 (US EPA 1998). This process involves measurement by thermal decomposition, reduction, amalgamation, and atomic absorption spectrometry. A quality control and assurance series was run every 12 samples. This series included the following: blanks (2), deionized water purge (1), dry purge (1), Apple Leaves 1515 (1) (NIST Standard Reference Material (SRM), US Department of Commerce National Institute of Standards and Technology, Gaither, MD 20899), MESS-4 SRM (1) (Marine Sediment SRM, National Research Council Canada, Lot G 4169010, Serial CC 567765) to verify recovery rates and sample integrity.
Social, Institutional, and Economic Analyses To examine how informal and formal governance structures mediate access to reef fish and their resource benefits, the governance research module combined data collected from both the HIES and the VRS surveys. As a representative sample of I-Kiribati households, the HIES provided standardized quantitative data on: household knowledge of and adherence to formal (e. g. , Government of Kiribati) and informal (e. g. , traditional, religious) fisheries rules and customs; inter-and intra-household dynamics of reef fishing and reef food access; prevalence and diversity of practices such as catch sharing and gifting; and how individual households with various characteristics utilize reef resources and access reef foods. Additionally, as a key informant survey of local village representatives, the VRS provided additional insights into: the specific rights, rules, and decision-making procedures applicable within a village; criteria for participation in various forms of village and fishery institutions, capital and assets available within the village or held in common, and historical context for the data provided by households through the HIES.
To examine how markets and trade mediate access to reef fish and their resource benefits, the market survey provided data on product availability, frequency of availability and price by village. We construct a product diversity score based on the market data and pair this with VRS data on proximity and travel time to other market centers. Together, these variables serve as an indicator of the market integration with formal markets in each village. We compare the prevalence of processed and high fat/high sugar foods available in the formal markets to the local produce and animal products sold within the village to measure the degree of nutrition transition of the supply. provided by our team's health professionals to the study subjects including access to free treatment and referrals to local clinics.
ACKNOWLEDGMENTS We would like to thank the following people: Tebwebweiti Tikanibwebwe (MHMS), Tebano Bwabwa (MHMS), Nantebwebwe Toabo (MHMS), Bwaturia Temaua (MHMS), Baurina Kaburoro (MHMS), Tirite Irooti (MHMS), Regina Flood (MHMS), Kiaman Raurenti (MHMS), Chris Holland (NOAA), Mark Vandersea (NOAA), Aaron Specht (HSPH), Agnes Yeeting (MFMRD), Tooreka Teemari (MFMRD), Max Peter (MFMRD), Taratau Kirata (MFMRD), Toaea Teawatei (MFMRD), Tebatei Kourabi (MFMRD), Karibanang Tamuera (MFMRD), Rateiti Vaimalie (MFMRD), Marouea Rabwerei (MFMRD), Toga Raikoti (SPC), Olivier Menaouer (SPC), Bertrand Buffiere (SPC), Nathalie Troubat (FAO), and Tiriara Ikam (MFED).
Acknowledgements The authors acknowledge the financial support of the " Bottom-Up Exploratory Program " of  CEA , the  French Alternative Energies and Atomic Energy Commission. The authors also thank  Fabrice Perrin  for the technical support on iCVD,  Géraldine Romano ,  Jérôme Dechamp  and  Laurent Bally  for the technical support on grinding and edge trimming,  Riadh Kachtouly  for the technical support on wet etching,  Laurent Gonon  and  Vincent H. Mareau  for their help on the Raman spectroscopy.
DNA Extraction and Illumina MiSeq Sequencing Fecal DNA was isolated using the protocol reported by Rodríguez-Nogales et al. [16]. DNA was amplified with primers targeting regions flanking the variable regions 4 through 5 of the bacterial 16 S rRNA gene (V4-5) and explored employing Illumina MiSeq technology. The PCR reactions from the same samples were pooled in one plate, cleaned, and normalized in Invitrogen SequalPrep 96-well Plate kit. The quantified sequences that resulted were completed, quality-filtered, clustered and taxonomically assigned on the basis of 97% similarity level against the RDP (Ribosomal Database Project)  [28]  with the QIIME2 software package (2021. 11 version; https://qiime2. org; California, USA) and "R" statistical software package (version 3. 6. 0; https://www.
Histology Colon specimens were fixed in 10% neutral buffered formalin for a minimum of 3 days, dehydrated, embedded in paraffin, and sectioned. Then, histological sections of 5 µm were stained with either hematoxylin and eosin (H&E) or Toluidine Blue (TB). The H&E histological staining was used to assess the inflammatory cell infiltration in the colon. On the other hand, TB staining was applied to visualize mast cells in the connective tissue. Specimens were evaluated independently by 3 individual researchers who were blinded to the experimental procedure. The results were analyzed using ImageJ Fiji Software (version 2. 9. 0. )  [27].
The questionnaire remained open until April 5, 2022. Reminders to fill out the survey were sent numerous times daily on the consortium's WhatsApp group while the survey was open. Among the 103 eligible protégées that received the link, 77 completed the survey yielding an overall response rate of 74. 7%. Once 2022 data collection was complete, all information linking individual participants to their survey responses was destroyed, creating a completely anonymous dataset.
ACKNOWLEDGEMENT Deep thanks to  WHO/TDR  and  IDRC Canada  for the technical guidance and financial support. Appreciations to the  Consortium for Mothers, Children , Adolescents and  Health Policy and Systems Strengthening  ( COM-CAHPSS ) for the numerous collaboration opportunities. Finally, we extend our gratitude to every single perso who has contributed (directly or indirectly) to the creation and sustainability of an environment that propels women in health research careers.
Wind farm flow control (WFFC) is a topic of interest at several research institutes and industry and certification agencies worldwide. For reliable performance assessment of the technology, the efficiency and the capability of the models applied to WFFC should be carefully evaluated. To address that, the FarmConners consortium has launched a common benchmark for code comparison under controlled operation to demonstrate its potential benefits, such as increased power production. The benchmark builds on available data sets from previous field campaigns, wind tunnel experiments, and high-fidelity simulations. Within that database, four blind tests are defined and 13 participants in total have submitted results for the analysis of single and multiple wakes under WFFC. Here, we present Part I of the FarmConners benchmark results, focusing on the blind tests with large-scale rotors.
A number of control-oriented models with different levels of complexity have been proposed in literature to implement those control strategies, but uncertainty remains high and a systematic validation and comparison under different control settings have been lacking. Validation and industrial implementation, in fact, is identified as one of the four key challenges within the WFFC field and thoroughly discussed in  Meyers et al. (2022) , where an in-depth review of the relevant studies is also presented. In order to assess the performance of the WFFC technology, the capabilities of WFFC models should be evaluated. Accordingly, the FarmConners consortium  (FarmConners, 2019)  has launched a common benchmark for code comparison, where high-fidelity simulation results, wind tunnel experiments, and field data measured at a full-scale wind farm are brought together.
This unique database combines the efforts of the connected WFFC projects of different sizes all over Europe and consists of four blind tests in total: (1) SMARTEOLE Sole du Moulin Vieux (SMV) field measurement campaign  (Duc et al. , 2019) , (2) CL-Windcon wind tunnel experiments  (CL-Windcon, 2016) , (3) CL-Windcon large-eddy simulations (LESs)  (CL-Windcon, 2016) , and (4) TotalControl LES  (TotalControl, 2018). Every data set is divided into a "calibration" and a "blind test" period to resemble field application of WFFC models. The calibration period involves both input and output features which can be used to calibrate the participating models under normal operation and limited control set points. In the blind test period, the calibrated models are to be run "blindly", where only the input features are provided and their outputs are compared against the validation data set or the blind test reference, as well as each other.
Promoting data sharing and standardization for validation processes as well, one of the most relevant exercises with similar structure is Wakebench  (Rodrigo et al. , 2014; Moriarty et al. , 2014) , focusing on wind farm flow modelling under normal operation. Although sharing similar goals, the FarmConners benchmark blind tests conduct the performance evaluation exclusively under controlled operation. Therefore, the two benchmarks diverge in terms of test cases, quantities of interests and the validation metrics. However, the lessons learned from the extensive Wakebench experience  (Doubrawa et al. , 2020)  is attentively taken into consideration while preparing the framework, aiming to extend the standard verification and validation (V&V) practices to include WFFC in wake research, globally.
Here in Part I, the modelling of the large-scale rotors (blind tests 1, 3, and 4) is investigated, and in Part 2 the blind test results of the wind tunnel experiments are presented. Accordingly, Sect. 2 describes the SMV field campaign and presents the participating model results for single-and multiple-wake scenarios under upstream wake steering. High-fidelity simulations being a key enabler for the industrial implementation of WFFC, the subset of the extensive CL-Windcon LES database used for the FarmConners benchmark is detailed in Sect. 3. The participating models for the CL-Windcon LES blind tests are then evaluated for three and nine-turbine wind farm configurations, with 5and 7-diameter (D) spacing respectively. Similarly, Sect. 4 utilizes up to eight-turbine subsets of a 32-turbine layout of a reference wind farm developed under the TotalControl project and compares the participating model results with the reference LES database under upstream wake steering.
Blind test 1: SMV wind farm field data The wind farm field data come from the Sole du Moulin Vieux (SMV) wind farm, located in the northern part of France (approximately midway between Paris and Lille) and operated by ENGIE Green. It consists of seven Senvion MM82 wind turbines (diameter of 82 m, nominal power of 2050 kW, hub height of 80 m; see  Duc et al. (2019)  for power and thrust coefficient, C T curves), organized in an irregular single row layout and labelled SMV1 to SMV7 from north to south. This wind farm has been used for the field tests of the French national project SMARTEOLE, whose results have been presented in  Ahmad et al. (2017)  and  Duc et al. (2019)  for field campaign 1 and in  Simley et al. (2021)  for field campaign 3. The layout of the wind farm is shown in Fig. 1 , and the long-term wind rose observed at the site is presented in Fig.
Participating models Within the FarmConners benchmark, in total five participants (IDs = P4, P5, P6, P16, P17) have taken part in the SMV wind farm field data blind test. The participating models cover a relatively broad range of assumptions, approximations, and parameters representing the flow behind a steered turbine. Here in this section, these participating models are briefly described, and implemented parameters are listed when relevant. Table  3  summarizes the prominent characteristics of the participating models. However, it should be noted that a seemingly identical model applied by the participants is likely to be calibrated differently, resulting in different performance in their predictions. This is further discussed in the detailed model descriptions per participant and highlighted in the blind test results later in the section.
P4 Wind speed and turbulence intensity. The wind speed was defined using nacelle anemometers at each wind turbine. The turbulence intensity was calculated using the mean and standard deviation of the anemometer data for each wind turbine. Wind direction. It has been assumed that the average of the directions indicated by turbines' wind vanes was a good estimate of the free wind. The multiple-wake simulations used the average of all wind turbines measurements, and the single-wake simulations averaged SMV5 and SMV6 direction data. Heterogeneous flow. The non-homogeneous flow field in the steady simulation was obtained through speed factors between turbines in free sectors (V g -velocity factor, TI g -turbulence intensity factor). The factors were obtained using the wind turbine anemometer measurements, and they were applied regardless of the wind direction.
P5 The baseline engineering flow model FLORIS  (NREL, 2021)  was adapted to the site by introducing parametric correction terms, which are learned from the available training SCADA data. Preparation of the calibration data set. The tuning of the engineering model was performed using the provided calibration data set. As FLORIS is a steady-state model, some of the SCADA data were discarded by looking at the nacelle position measurements, with the aim of only using data characterized by fairly steady and uniform inflow and operating conditions. Specifically, a 10 min SCADA data point was discarded if the variation in the nacelle orientation of any turbine between two consecutive timestamps exceeds 20 • , so as to discard data measured under strongly varying wind direction; the deviation of the nacelle orientation of any turbine from the average pointing direction of the entire wind farm exceeds 20 • , so as to discard data measured under highly non-homogeneous wind direction; the standard deviation of the nacelle orientation of any waked turbine was not null, so as to discard data recorded while one of the waked turbines was yawing during the 10 min period.
Air density. Historical atmospheric data have been obtained from ERA5 and NEWA reanalysis data sets at the closest available nodes to the site. The long-term averaged air density at the site was found to be 1. 23 kg m -3 , with values ranging between 1. 13 and 1. 35 kg m -3. These values have been used to correct the power curve in the simulations (see below). Wind shear. The lidar measurements were provided at different heights and for a period of about 8 months. The two measurements closest to the low tip and high tip of the rotor (40 and 120 m respectively) have been used to estimate the average shear profile across the turbine rotor. Assuming the directional wind shear estimations at the lidar location are representative for all turbine locations (except for the directions with the wake effects), power-law exponents 0.
Validation data pre-processing The wind farm field data blind tests are prepared as single and multiple wakes, where the upstream turbine SMV6 was steered with -13. 3 • mean with respect to the incoming south-westerly wind. The results are evaluated in terms of balanced energy gains, as defined in  Fleming et al. (2019). Since the experiments were not realized in the form of a toggle test, the baseline case is taken by considering two 2month periods before and after the field tests, as shown in Fig. 11. The wind speed and direction reference signals for computing the energy ratios come from the Windcube v2. Due to the proximity of this sensor with the controlled turbine, the atmospheric conditions measured should be very similar to the ones faced by SMV6. The reference power signal is issued from the SMV7 wind turbine, which is the only remaining upstream turbine for the southerly wind sector.
Summary of the wind farm field data blind test Although it is the key priority for advancing wind farm control technology  (van Wingerden et al. , 2020) , the field test and validation for WFFC-oriented models are significantly challenging due to the stochasticity, non-stationarity, high variability, and overall uncertainty. Specifically for the FarmConners benchmark, the highlights of the participating model performance for the wind farm field data blind test can be summarized as below. -Similar models, disparate behaviour. In this blind test, several participants implemented similar models to resolve the wake behaviour behind a steered turbine (as listed in Table  3 ). However, even for the same framework utilized by P4 and P5, the results are seen to notably differ. This indicates high model sensitivity to the employed parameters and emphasizes the importance of the calibration procedure, as also analysed for another wind farm by  van Beek et al.
Given the high sensitivity of the parameters on the prediction of the gains observed, it can be concluded that the wake steering solution in practice would/should not be designed solely based on normal operation data. It would/should rather follow an iterative process in which a priori strategy can be developed and implemented based on normal operation tuning or standard, recommended, or off-the-shelf parameter values. After For the whole data set, combinations of three wind speeds, turbulence intensities, and roughness lengths were applied together with varied wind turbine control settings. However, for the specific blind test in FarmConners benchmark, an inflow corresponding to an average wind speed of 7. 7 m s -1 , turbulence intensity of 5. 39 %, and roughness length of 0. 001 m was selected (referred to as A4 in the original database CL-Windcon, 2019).
The CL-Windcon LES blind test is among the most diverse and comprehensive in terms of the wake steering control strategies applied within the Farm-Conners benchmark. The yaw misalignment was varied in the range of ±30 • for the first and/or the second row of turbines; see Table  7  for particular control settings among the 3WT and 9WT scenarios. The direction for the yaw convention, as well as the distribution of the control set points among the turbines, is illustrated per investigated case in the presentation of the results later in Sect 3. 2 and 3. 3. Targeted test cases explore a single full wake and multiple wakes under wake steering WFFC. Available data cover power production, hub-height, and/or rotor effective wind speeds at the turbines, as well as several structural loading variables, namely flap-wise root bending moment, total shaft bending moment, and total tower bottom bending moment.