dataesr/scientific-paragraphs-categorization huggingface dataset:
Study protocol This systematic review was conducted according to criteria of the Preferred Reporting Items for Systematic Review and Meta-Analyses (PRISMA) 2020 statement. 13  A literature search was performed using the Embase, MEDLINE, Cochrane, and Scopus databases. The following search terms were used: ("stereotactic body radiotherapy" OR "SBRT" OR "SABR" OR "stereotactic ablative radiotherapy") AND ("hepatocellular carcinoma" OR "HCC"). Full text articles published in the English language up until October 2022 were identified. The initial query identified 3085 articles, which were subsequently screened for relevance to the objectives of the present study by thorough review of the article titles, abstracts, and full texts as necessary. Two reviewers (SJ Chun and JH Chung) independently performed the search and screened studies to identify eligible studies, with significant discrepancies settled by an independent third reviewer (SH Bae).
Selection criteria The following inclusion criteria were used: (1) clinical studies including retrospective or prospective studies specific to liver-confined HCC; (2) inclusion of >10 patients with HCC treated with SBRT; (3) SBRT performed in <10 fractions; and (4) reporting of at least ≥5 years LC and/or OS rates. When numerical data were absent, LC and/or survival rates were indirectly estimated from the descriptive plots. In cases of multiple studies from one institution with overlapping patients, the following criteria were applied, to determine inclusion and are prioritized by numerical order: (1)  study that reports exclusively on treatment outcomes of patients with HCC after SBRT; (2) study with the largest number of patients; and (3) most recently published study. Studies from the same institution were independently categorized if they were conducted in different periods.
Data extraction and quality assessment Data extraction was performed using a standardized form with the following data obtained: (1) study, patient, and tumor characteristics; (2) treatment; and (3) survival and (4) hepatic toxicity rates. Survival included 1-to 5-year survival rates, which were either reported in the studies or derived from the survival curves. Hepatic toxicity was defined according to Common Terminology Criteria for Adverse Events, version 4 or 5, and/or radiation-induced liver disease (RILD), of which there are 2 types: classic RILD and nonclassic RILD. Because most (16/17) of the included studies were retrospective, the Newcastle-Ottawa Scale (NOS) was used to assess the quality of the included studies. 14  Studies with a score of 7 to 9 are considered high quality and studies with a score of 4 to 6 are considered medium quality.
Statistical analysis For the aggregated data (AD) meta-analyses, the heterogeneity among the studies was assessed by Higgins I 2 statistic. 15  An I 2 value of ≥50% was considered to represent substantial heterogeneity. Given the variation in treatment decision making, periods for which the study was applicable to, and geographic location, which influences etiology, the random effects model was considered superior to the fixed effects model when calculating pooled estimates. The DerSimonian and Laird method was used for random-effect analysis, and we report both estimates in the tables. 16  Publication bias was assessed by funnel plots and the Egger's regression tests. If the funnel plot was symmetrical or the P value was >. 05 in Egger's test, then the null hypothesis of no publication bias was accepted.
For comparison between subgroups, a Q test based on analysis of the variance and random effects model was used. Values of P <. 05 were considered statistically significant. All statistical analyses were performed using Rex Excel-based statistical analysis software, version 3. 6. 0 (RexSoft, Korea, http://rexsoft. org/). For the IPD analysis, survival was estimated using the Kaplan-Meier method and compared between groups using the log-rank test. All statistical analyses were performed using Statistical Package for the Social Sciences software (version 27. 0; SPSS Inc, Chicago, IL), and a value of P <. 05 was considered statistically significant.
Results An initial search of the 4 databases provided a total of 3085 studies. After removing 746 duplicate articles and 1239 irrelevant articles, 1100 studies were selected for title and abstract screening, of which 238 studies were selected for a full-text review. Finally, 17 studies comprising 1889 patients were found to fit the inclusion criteria for the AD metaanalysis. Among these, 6 studies comprising 665 patients were included for the IPD analysis. The selection process is summarized in Fig. 1. All 17 studies were either retrospective or prospective observational studies. Therefore, the quality of the studies according to the NOS criteria was rated medium. Most were conducted in Eastern countries (China, Japan, Korea, Saudi Arabia, Taiwan, and Thailand), and 2 were conducted in Western countries (Canada, United States, and France).
IPD analysis: Treatment outcomes Six hundred sixty-five patients from 6 studies were included in the IPD analysis. [18] [19] [20] 26, 30, 33  Roquette et al  18  provided   Abbreviations: BCLC = Barcelona Clinic Liver Cancer; BED = biologically effective dose, which was calculated using an a/b ratio of 10; fx = fractions; LC = local control; NR = not reported; OS = overall survival; PFS = progression-free survival; PVI = portal vein invasion; SBRT = stereotactic body radiation therapy. * Patients with BCLC stage 0 and A. y Divided into 3 groups: BCLC stage 0 and A versus B versus C and D. data for 317 of the 318 patients initially included in their report, as 1 patient withdrew consent after the date of publication. The 3-year and 5-year LC rates were 80% (95% CI, 0. 75-0. 85) and 79% (95% CI, 0.
The ongoing phase 3 NRG Oncology RTOG 1112 trial (NCT01730937) using effective liver volume to aid SBRT dose allocation could be an answer. To our knowledge, this is the first report demonstrating differences in survival rates after SBRT for HCC according to geographic location. On AD meta-analysis, 5-year OS rates were better for patients from Eastern regions than for those from Western regions (43% [95% CI, 34%-53%] vs 17% [95% CI, 6%-32%], P =. 0035). However, LC rates tended to increase in Western regions compared with Eastern regions (90% [95% CI, 82%-97%] vs 80% [95% CI, 71%-88%], P =. 0671). We hypothesize that this potential difference may be related to different etiologies of HCC based on region. Hepatitis B virus (HBV)-and hepatitis C virus (HCV)-induced HCC occurred in 68% to 100% of patients from Eastern regions, as opposed to 12% to 52% from Western regions (Table  1 ).
This work was supported by a grant ( 50572-2023 ) from the  Korea Institute of Radiological and Medical Sciences (KIRAMS) , funded by the  Ministry of Science and ICT (MSIT), Republic of Korea. This work was also supported by the  Soonchunhyang University Research Fund. No funding bodies had any role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. We acknowledge NHS funding to the  National Institute for Health Research (NIHR) Biomedical Research Centre  at the  Royal Marsden  and the  Institute of Cancer Research. The views expressed in this publication are those of the authors and not necessarily those of the NHS, the  NIHR , or the  Department of Health and Social Care. Disclosures: A. C. T. acknowledges the support of  Cancer Research UK  (grant numbers  C7224/A28724  and  C33589/A28284 ).
Collecting and processing patrol data Ranger patrols were conducted in the contiguous Hue and Quang Nam Saola Nature Reserves by Forest Guard teams, coordinated by World Wide Fund for Nature (WWF), together with local Forest Protection Department staff. Threat data was input into Management Information System (MIST) and Spatial Management and Reporting Tool (SMART) systems. MIST and SMART data were curated and cleaned to create a database for analyses. We used patrol tracklog data to calculate monthly patrol effort in a grid of 200 x 200 meter cells. Data for traps were converted into detection/non detection matrices using monthly occasions using the same grid for analysis with occupancy models  (MacKenzie et al, 2017). See Supporting Information.
Acknowledgements We thank the staff of the  WWF-CarBi  project (phase I and phase II), the  Thua Thien Hue Saola Nature Reserve , and the  Quang Nam Saola Nature Reserve  for supporting data collection, including through providing personnel for snare removal. The CarBi projects were and are still funded by the  International Climate Initiative of the Federal Ministry for the Environment, Nature Conservation, Nuclear Safety and Consumer Protection (BMUV)  through the  German Development Bank (KfW). A. T. would like to thank  Barney Long  for productive discussions over the years on snare removal within the context of protected area management,  Julie Louvrier  for advice on study design and analysis, and  Lucy Emerton  for input on protected area financing in Vietnam.
Data source The ESC-HF-LT-Registry was conducted by the EORP in 33 ESC member countries including 337 centres, to study the characteristics and outcomes of patients with HF in Europe and surrounding regions between 2011 and 2018. 13  The registry enrolled in-and out-patients with HF (either pre-existing or new-onset HF) on a one-day-perweek basis. Automated electronic data checks were performed. The electronic case report form (eCRF) contains information on several patients' characteristics, clinical data, laboratory measurements, and imaging parameters collected at baseline (at hospital admission or out-patient visit) and also during in-hospital course for in-patients. Data on outcomes (hospital admissions and mortality) were assessed at approximately 12 months. Patient management followed local diagnostic and therapeutic practices.
9  All patients provided written informed consent, and the registry was approved by local ethical review boards according to the regulations of each participating country. The current analysis included 25,154 patients enrolled from 2011 to 2018. In hospitalized patients with variables collected at different time points, the discharge measure was used. In-patients who died during the hospitalization were excluded. For EF we used the measurement recorded as an integer percentage number by Echo-Doppler during the hospitalization (for in-patients), or linked with the out-patient visit. For the outcome analysis, only 19,080 patients with follow-up data were considered.
In patients with missing information for the date of hospitalization, the time to hospitalization was imputed with half the time to the last follow-up. If the time to hospitalization was longer than the time to death or to last followup, it was set to death or last follow-up. Patients with unknown cause of death were considered to be non-CV death. The time to event was illustrated by cumulative incidence curves. Hazard ratios (HR) with 95% confidence intervals (CI) were calculated by Cox proportional hazards regressions to compare the crude risk of outcomes across the EF spectrum, and in patients with vs without digit bias and missing EF. Incidence rates per 100 patientyears with 95% Poisson CI were also calculated. All analyses were performed using the R code for the data management and statistical analyses ( https: //github.
19  Apart from the reclassification of HFmrEF and consequences for treatment indications, there may be other important consequences of or associations with digit bias. Notably, patients with a potential digit bias were more likely to have missing data for several patient characteristics linked with their medical history, HF symptoms and signs, echocardiographic parameters, and laboratory measurements, which might mean that these information were not collected or recorded. They were more likely older, had higher comorbidity burden, more severe HF, lower EF and renal function, and were less likely to receive HF pharmacological and device treatments as compared with those with an EF not ending with 0 or 5, which translated into a higher risk of morbidity/mortality, suggesting that a lack of rigor may be associated with these and other aspects of suboptimal care, leading to a worse outcome.
The study used Landsat-8 Operational Land Imager (OLI) images and a training soil dataset of 200 samples collected over the area. The approach consists in four steps:  (1)  studying relationships between the MID index and the measured soil clay content over the plain, (2) applying Ordinary cokriging (OK) as interpolation technique to obtain complete coverage of topsoil clay even in the vegetation zones, (3) validating the resulting map based on independent soil samples sets, (4) using PTFs based only on clay content information to derive the hydraulic conductivity, the field capacity and the wilting point.
Soil sampling and analyses A total of 200 samples were taken from various pedological formations at the topsoil (0-15 cm); 140 in the agricultural zones and 60 over the bare soil (Fig. 1 ). The samples were packed in plastic bags and properly marked for identification and analyzes. 20 g of the mixed soil was sampled for analyzing grain size distribution. These soil samples were air-dried and sieved in two fractions: (0. 05-2 mm) to calculate the percentage of sand content. While the smaller fraction passed through (0. 05 mm) sieve was recovered and collected in vial then analyzed using pipette and/or granulometry laser methods to measure coarse loam (20-50 μm), fine loam (2-20 μm) and clay content (< 2 μm). The soil samples were classified according to the classification system of the USDA, using the Talwin 42 software.
Selection of the appropriate Landsat-8 images The images download from http://earthexplorer. usgs. gov/ were processed geometrically and atmospherically for 30 m spatial resolution by the MUSCATE software using CNES computing center (add reference for this software). The products are ortho-rectified surface reflectance after atmospheric correction, along with a mask of clouds and their shadows, as well as a mask of water and snow. The atmospheric correction and the cloud detection were processed using the MACCS processor, developed at CESBIO  [Hagolle et al. , 2010]. The top-of-atmosphere reflectance was converted to surface reflectance then used to calculate normalized difference vegetation index (NDVI) and mid infrared index (MID). The use of remote sensing for soil studies faces 2 issues: (i) the presence of green or dry vegetation that covers the soil surface  [Wang et al.
This parameter is estimated as the difference between (θ fc ) and (θ wp ) as mentioned in  [Kirkham, 1972] : MID -Infrared = TM6-TM7 TM6+TM (1)   (17)  To validate the prediction of θ fc , θ wp and K sat , and to check the accuracy of the applied formulas, two different datasets of test fields were used (4) (Fig. 6 ). The first one corresponds to a soil sampling on the experiment sites of Chichaoua, Aitimour, Saada, Agafay, Agdal, R3 and Sidi Rahal (Fig. 1 ). 𝛾𝛾 (𝑢𝑢) =.
Acknowledgments This research was conducted within the Joint International Laboratory-TREMA (https://www. lmitrema. ma/). It was supported by the projects:  SAGESSE PPR/2015/48  " Système d'Aide à la décision pour la   GEstion des reSSources en Eau ",  ERANETMED3-062 CHAAMS "global CHange: Assessment and Adaptation to Mediterranean region water Scarcity ",  ACCWA-823965/H2020-MSCA-RISE-2018  " Accounting for Climate Change in Water and Agriculture management ",  PRIMA-ALTOS "Managing water resources within Mediterranean agrosystems by Accounting for spatiaL sTructures and cOnnectivitieS" and   PRI-MA-IDEWA "Irrigation and Drainage monitoring by remote sensing for Ecosystems and Water resources management ".
fc and θ wp is obtained based on the pedotransfer function of Wosten et al. (2001) [Er-Raki et al. , 2007, 2010; Khabba et al. , 2013; Nassah et al. , 2017]. While the second datasets were used by extracting 38 points from the textural map of the plain of Haouz (Fig. 3), whose textural class of each point is derived from the hydrodynamic properties of the soil by using FAO-56 which describes the physical characteristics of the soil for various soil type [Dirk et al. , 2012].
1. Passive acoustic monitoring has become increasingly popular as a practical and cost-effective way of obtaining highly reliable acoustic data in ecological research projects. Increased ease of collecting these data means that, currently, the main bottleneck in ecoacoustic monitoring projects is often the time required for the manual analysis of passively collected recordings. In this study we evaluate the potential and current limitations of BirdNET-Analyzer v2. 4, the most advanced and generic deep learning algorithm for bird recognition to date, as a tool to assess bird community composition through the automated analysis of large-scale ecoacoustic data. 1 2. To this end, we study 3 acoustic datasets comprising a total of 629 environmental soundscapes collected in 194 different sites spread across a 19° latitude span in Europe.
We analyze these recordings both with BirdNET and by manual listening by local expert birders, and we then compare the results obtained through the two methods to evaluate the performance of the algorithm both at the level of each single vocalization and for entire recording sequences (1, 5 or 10 min). 3. Our analyses reveal that BirdNET identifications can be highly reliable if a sufficiently high minimum confidence threshold is used. However, the current recall of the algorithm is markedly low when the minimum confidence threshold is adjusted to ensure high levels of precision. Thus, we found that F1-scores remain moderate (<0. 5) for all datasets and confidence thresholds studied. We therefore estimate that acoustic datasets of extended duration are currently necessary for BirdNET to provide a reliable and minimally comprehensive picture of the target bird community.
Moreover, the review suggests that BirdNET performance should be studied not only at the entire recording level but also at the level of each single identification/vocalization. Last but not least, BirdNET detection capacity (i. e. , recall rate) across a large range of European soundscapes has yet to be investigated. To fill this knowledge gap we analyze, both automatically with BirdNET and manually by expert birders, 629 environmental soundscapes collected in 194 different sites spread across a 19° latitude gradient in Europe (Fig. 1 ). We then compare the identifications obtained with BirdNET against those made by experts in order to assess the performance of the algorithm in terms of precision and recall (see subsection 2. 4. 1). Subsequently, we examine how to optimize the existing trade-off between these two metrics and how different features of the acoustic dataset analyzed might influence BirdNET performance.
2. 1. Study area and soundscape collection The environmental soundscapes analyzed in this study originated from three independent acoustic datasets (Table  1 , Appendix S1). Our first dataset, hereafter referred to as ZA-Pygar, consists of a total of 237 1-minute recordings collected from 2019 to 2022 in 79 different sampling sites in the French region of Occitanie  (Barbaro et al. , 2022). More precisely, the region sampled is part of the Zone Atelier Pyrénées Garonne  (Ouin et al. , 2021) , considered a long-term socio-ecological research site by French research institute CNRS since 2017. Out of the 79 sampling sites from which recordings were obtained, 60 are found in the Aurignac canton, 16 are found either in alpine meadows or at the edge between alpine meadows and subalpine forests at altitudes between 1400 and 2100 m in the Ariège department, and the remaining 3 are found in lowland agricultural areas in South-West Occitanie (Fig.
1a ). Our second dataset, hereafter referred to as Rambouillet, consists of a total of 204 5-minute recordings collected during the COVID-19 lockdown of 2020 in 68 different sampling sites in the public forest of Rambouillet (Fig. 1b ). This lowland sub-Atlantic broadleaf forest, covering 220 km², is located in South-West Île-de-France (France), about 70 km from Paris  (Barbaro et al. , 2023). Finally, our third dataset, hereafter referred to as TreeBodyguards, was inherited from a pan-European citizen science project and consists of a total of 188 10-minute recordings collected in 47 different sampling sites from 17 European countries ranging from Spain to North-West Russia (Fig. 1c )  (Valdés-Correcher et al. , 2021).
2. 2. Manual bird identification by experts Bird vocalizations have been identified by expert birders in the three datasets. In the ZA-Pygar dataset, all bird sounds found within the first minute of each of the 237 recordings were manually identified and annotated by a single trained birder (DF) by drawing time-frequency bounding boxes around every single vocalization on recording spectrograms (Appendix S2, Fig. S1 ). In the two other datasets, a list of species present in each recording was generated after the listening, the specific vocalization times of each species remaining unknown. In the Rambouillet dataset, each 5-minute recording selected was manually listened to once, all species detected being listed regardless of their total vocalization time by a single trained birder (LB). As for the TreeBodyguards dataset, given its wide geographical range, we distributed the recordings among 21 expert birders (see Acknowledgements), each of them manually listening to and identifying bird sounds from between 4 (one site) to 52 recordings (13 sites).
BirdNET-assisted analysis of recordings The BirdNET version chosen for this study is BirdNET-Analyzer v2. 4, the most recent release of the algorithm at the time of publication. BirdNET takes as input not only a sound file but also its approximate recording date (in the form of week of the year) and geographic coordinates (Appendix S3). It then splits the input recording into 3-second segments and provides a list of species detected in each segment along with a confidence score assigned to each identification, with values ranging from 0 to 1. By default there is no overlap between consecutive prediction segments, i. e. , each segment begins where the previous segment ends, but BirdNET offers the possibility of allowing a certain degree of overlap (Fig. S2 ). In this study we initially performed the same analyses with allowed overlaps of 0 s, 1 s and 2 s between consecutive segments, and the solidly higher performances obtained with the latter option on the ZA-Pygar dataset (Table  2 ) made us choose it as the input value to evaluate BirdNET with in all datasets.
2. 4. Comparing BirdNET and expert identifications We compared the results obtained with BirdNET against the identifications made by the expert birders in order to evaluate the performance of the algorithm. BirdNET results corresponding to the ZA-Pygar dataset could be analyzed at a higher level of detail than those obtained for the other two datasets, since its temporally annotated identifications make it possible to know the specific vocalization times of each species. For our evaluation we defined 4 possible categories -True Positives (TP), False Positives (FP), True Negatives (TN) and False Negatives (FN)-for each species and acoustic sample, the categorization criteria being slightly variable among the three acoustic datasets analyzed (Table  S1 ). Based on the categorization described, we calculated the precision, the recall and the False Positive Rate (FPR) of the algorithm for each minimum confidence threshold studied, going from 0.