dataesr/scientific-paragraphs-categorization huggingface dataset:
We retrieved and downloaded the data from the NASA/USGS Land Processes Distributed Active Archive Centre (https://lpdaac. usgs. gov). The data acquisition dates were between 2019 and 04-22 and 2020-04-14, the latest data available at the time. The bounding box used to capture spatial GEDI footprints has the following WGS84 coordinates: 1. 417434  (xmin), 2. 792492 (xmax), 47. 11044 (ymin), 48. 1175 (ymax). The BDForêt® V2 forest is a vector product derived from the interpretation of near-infrared aerial images and it provides information of vegetation composition  (Vega et al. , 2021). For instance, it captures forest stands of at least 0. 5 ha in size. The forest mask was mainly used to filter out irrelevant GEDI footprints, i. e. , whether or not GEDI footprints are located in vegetation.
Methods The flowchart below (Fig. 2 ) illustrates the overall framework of this study. In brief, we adopted the framework of a two-phase sampling scheme described in  Hill et al. (2021). We first had to select the suitable GEDI RH metric that can represent the NFI dominant height at plot level. Based on a semi-variogram examination, we paired GEDI auxiliary information with NFI measurements at plot level up to a critical distance obtained from the semi-variogram. Finally, small area estimation of forest volume was performed with a simple linear regression using the R package "forestinventory".
Variogram examination of the GEDI values To explore the spatial correlation of the GEDI footprints, the semivariance of the selected GEDI RH was calculated following Eq. (  1 ) and the corresponding empirical semi-variogram was established using the variogram function from the 'gstat' package  (Pebesma, 2004). The selected RH was used as a surrogate of the field dominant height, with the aim of obtaining an idea of the spatial dependence of the canopy cover present in the AOI, which can help to determine the optimal distance threshold used to pair GEDI footprints with corresponding NFI plots. Semivariance = 1 2n × ∑ n i∕ =j ( RH x,i -RH x,j ) 2 (1) where n is the number of GEDI footprints, RH x, i and RH x, j are the selected RH x values of GEDI footprints recorded at 2 separate locations i and j.
Small area estimation Unit-level SAE was performed using the two-phase non-exhaustive estimation procedure provided in the R package "forestinventory" and described in  Hill et al. (2021). The first phase is associated with the auxiliary GEDI information used to generate model predictions based on a linear regression using the method of ordinary least squares. The second phase contains the field NFI plot attributes (i. e. , forest volume), that was used to generate model coefficients and correct bias. Using field data alone (all available plots within the AOI, not restricted to the paired plots), the mean and variance of the SRS were calculated as Eqs. (  2 ) and (3): μSRS = 1 n ∑ n i=1 y i (2) Var ( μSRS,G ) = 1 n × (n -1) ∑ n i=1 (y i -μSRS ) 2 (3) where n is the total number of NFI sample plots in the AOI in this case, and y i is the observed value of forest volume of plot i.
Small area estimation of forest volume With the NFI-GEDI pairs and filtered GEDI RH 98 , small area estimation of forest volume was performed at both AOI and sub-area levels using a simple linear internal model (i. e. , built from the paired plots of the small area). Results showed that GEDI auxiliary information slightly improved the estimation accuracy for the AOI level at the maximum distance threshold used for pairing (i. e. , 500 m). A similar increase in relative efficiency was also witnessed in every sub-area with an increasing number of NFI-GEDI pairs used to build the models (Table  3 ). At department level, when limiting the spatial distance between paired NFI plots and GEDI footprints to 100 m, there was no gain in relative efficiency observed in all three departments. This meant that GEDI auxiliary data were not able to reduce model variance under this condition as compared to the standard NFI sample.
Acknowledgements We are grateful to the two anonymous reviewers for their valuable comments on improving the quality of this manuscript. This research received the financial support of the  TOSCA Continental Surface program  of the  Centre National d'Etudes Spatiales (CNES)  through the project  SLIM  " Space Lidar for Improved Multisource Forest Inventory " ( grant number  4800001129  ). IGN and the  ONF Département RDI  are supported by the  French National Research Agency (ANR)  as part of the  "Investissements d'Avenir" program  ( ANR-11-LABX-0002-01 ,  Lab of Excellence ARBRE). SZ also received financial support from the  Academy of Finland Flagship Programme (Forest-Human-Machine Interplay -Building Resilience, Redefining Value Networks and Enabling Meaningful Experiences (UNITE) ; grant number  337127 ).
Results Figure  1  displays the Marginal Means (MM) using the sample for all features and attribute levels. We follow recent advances in the reporting of conjoint experiment results  (Leeper et al. , 2020)  and present the results using Marginal Means. We analyzed the data using RStudio (RStudio Team, 2021) and the cregg R-package  (Leeper, 2018). Estimates represent the preferences of the average YV protester while the gray vertical line shows the 0. 5 probability of a candidate being chosen, that is, the grand mean probability in a choice task setting with two profiles. Thus, estimates to the right of this line (i. e. , with a value above the 0. 5 threshold) indicate that the presence of a given feature increases candidate support, whereas estimates to the left indicate that the attribute level has the opposite effect and leads to a higher chance of rejection of the candidate profile.
The main reason behind these smaller numbers is the overall diversity of these groups: ~1000 extant gymnosperms, ~1000 extant lycophytes, and 10,000 extant ferns. These numbers are substantially smaller than the estimated ~300,000 flowering plants  35, 36. In addition, ferns are believed to have lower levels of endemicity, and gymnosperms are more diverse in northern latitudes and not in the mostly tropical megadiverse countries  37. There may also be other data biases for these groups that are less studied than their flowering counterparts. Nonetheless, flowering plants are by far the most abundant group in our dataset-15,162 species-and are nevertheless vulnerable in terms of complete loss of species' geographic ranges (Fig. 3  and Supplementary Data 3). The decline of flowering plant diversity across megadiverse countries will likely increase the extinction risks of other ecologically linked groups  4, 38  , even when these appear to have an intrinsically lower vulnerability to climate change (i.
Methods Species occurrence records. We compiled data on the distribution of 21,252 endemic species of any of the twelve megadiverse countries from four tetrapod (5,757) and four vascular plant groups (15,389) (amphibians, reptiles, birds, mammals, lycophytes, ferns, gymnosperms, and flowering plants). Species occurrence records were obtained from the Global Biodiversity Information Facility (GBIF)  27  , the International Union of Conservation of Nature (IUCN)  28  , and BirdLife  60, 61. We only modeled species with at least 25 unique records at a 5 arcminute resolution (~10 km at the equator). In many cases, the processing of the IUCN polygons resulted in species with thousands of occurrence records. In these cases, we randomly chose a maximum of 500 records per species. The greater the number of observed records, more problems can be associated with spatial bias in the modeling  62.
We converted all selected species polygons into unique records at a 5 min resolution (~10 km at the equator). For vascular plants, we used geographic occurrence data obtained from the Global Biodiversity Information Facility by querying all records under "Tracheophyta" (we only considered "Preserved Specimens" in our search). Plants records were taxonomically homogenized and cleaned following the procedures described in ref. 63  using Kew's Plants of the World database  64  as the source of taxonomic information. Mostly, we identified endemic species as those with all occurrence records restricted to any given megadiverse country. For countries in which data for vascular plants were scarce or absent (e. g. , India), we complemented occurrence information with polygons from the IUCN (although IUCN data for plants remains limited) following the procedure described for tetrapods.
The time horizons evaluated represent short, medium, and long terms in order to help decision-makers order conservation priorities. Ecological niche modeling. At their most basic, the algorithms used to construct species distribution models relate species occurrence records with climatic variables to create a climatic profile that can be projected onto other time periods and geographic regions  66. The resulting models have proven useful in evaluating the impacts of climate change on biodiversity and to identify varying levels of vulnerability among species  32, 67, 68. Here, we employed a multi-algorithm (ensemble) approach to construct species distribution models as implemented in the "bio-mod2" package  67  in R 69 (Supplementary Fig. 33 ). The underlying philosophy of ensemble modeling is that each model carries a true "signal" about the climateoccurrence relationships we aim to capture, but it also carries "noise" created by biases and uncertainties in the data and model structure  32, 67.
By combining models created with different algorithms, ensemble models aim at capturing the true "signal" while controlling for algorithm-derived model differences; therefore, model uncertainty is accounted for during model construction (see Supplementary Material for further detail). Prior to modeling, we reduced the number of bioclimatic variables per species by estimating collinearity among present-day bioclimatic variables. We employed the "corrSelect" function of the package fuzzySim  70  in R  69  , using a Pearson correlation threshold of 0. 8 and variance inflation factors as criteria to select variables. Given the number of species evaluated and the ecological information scarcity, we did not select a set of variables based on ecological knowledge by each of the species modeled. Instead, for the variables pre-selection, we used the statistical approach described above that has been proven to give models with good performance  71, 72.
We used seven algorithms with a good predictive performance (evaluated with the TSS and ROC statistics; Supplementary Fig. 1 ): Maxent (MAXENT. Phillips), Generalized Additive Models (GAM), Classification Trees Analysis (CTA), Artificial Neural Networks (ANN), Surface Range Envelope (SRE), Flexible Discriminant Analysis (FDA), and Random Forest (RF). Because occurrence datasets consisted of presence-only data, for each model, we randomly generated 10,000 pseudo-absences within the model calibration area; we gave presences and absences the same importance during the calibration process (BIOMOD's prevalence = 0. 5). For each species, we selected a calibration area (i. e. , the accessible area or M)  73  using a spatial intersection between a 4°buffer around species occurrences and the terrestrial ecoregions occupied by the species  73  (Supplementary Fig.
Acknowledgements We are grateful to  Francisco Estrada-Porrúa ,  Julián A. Velasco , and the members of " Clima y Sociedad"  for the ideas that prompted the development of the project and comments on earlier drafts. Part of the analyses in this paper were carried out on CONABIO's ( Comisión Nacional para el Conocimiento y Uso de la Biodiversidad ) computing cluster, supported by their system administrator and the  Subcoordinación de Soporte Informático ; and the other part of the analyses in this paper were carried out on the computing cluster  Tláloc (Centro de Ciencias de la Atmósfera)  supported by their system administrator.
Data availability Data for species distribution models are available at Zenodo with the identifier https:// doi. org/10. 5281/zenodo. 4917258. The geographic occurrence data for vascular plants is available from the Global Biodiversity Information Facility with the identifier https://doi. org/10. 15468/dl. bdxzkw. The distribution polygons for tetrapods and vascular plants are available at https://www. iucnredlist. org/ and http://www. birdlife. org/.
Baidu Search Term Selection Potentially relevant search terms were determined using the procedure illustrated in Figure  1. Primary indicator terms to identify more related queries about seasonal influenza were found in 2 data sources. The first was an open-access Chinese website  [16] , a free platform that provides internet keyword mining of the Baidu search engine in mainland China. The second was the Baidu index website. Overall, 107 keywords were included in the primary database. After excluding non-flu-related news and movies and words related to the pandemic, avian influenza, COVID-19, and those specific to certain regions, 37 words were retained.
E[Yt] refers to the time series of the daily influenza-positive rate in northern and southern China, and cb(BI) refers to the cross-basis matrix of the time series of daily Baidu search terms, respectively. The natural cubic spline function was adopted in all the element spaces, and a lag period of 1-20 days was selected when establishing a cross-basis matrix. The quasi-Poisson function was used as the connection function in the model to control the overdispersion effect. Relative risks were used to assess the lag-response relationships of single-day effects. Before DLNM analysis, all Baidu search terms were divided into 4 categories: general influenza information, influenza prevention, influenza treatment and medicine, and influenza symptoms. The DLNM was used using R Studio and R software (version 4.
Moreover, to prevent the disappearance of the gradient, after splicing the results of the different attention modules, we also connected the results with 2 pooling layers for residual connection and finally through the dense layer to output the predicted results. The training interval was from January 1, 2011, to May 31, 2017, the validation interval was from June 1, 2017, to August 31, 2017, and the prediction interval was from September 1, 2017, to July 31, 2018. According to the PCCs, the Baidu search terms were classified into 4 levels, namely, correlation (P<. 05), regular correlation (PCC≥0. 4), and strong correlation (PCC≥0. 6-0. 7). The R 2 and SE were calculated by comparing the predicted and actual data to identify the best model with different correlation levels of Baidu search terms.
Correlation Analysis Between Baidu Search Terms and Influenza-Positive Rate Based on the literature and expert consultation, the Baidu search terms we screened out were all related to influenza. Overall, we observed a high correlation between the Baidu search terms and the influenza-positive rate in all databases except the term 胸 闷 ("sense of suppression in the chest"). The terms were classified into 4 categories: basic understanding of influenza, symptoms, treatment and medicine, and prevention (Table  1  and Figure  S1  in Multimedia Appendix 1). The correlation coefficients differed between northern and southern China. The lag of relevant days varied according to the search term category. Search terms related to the basic understanding of influenza, treatment, and medicine showed no associated lag.
We analyzed the correlation between the Baidu search terms and the seasonal influenza-positive rate, and terms related to basic influenza knowledge, symptoms, treatment and medicine, and prevention were found to be highly correlated with the seasonal influenza-positive rate. However, search queries may vary by country and language. A study on GFT found that 26 Dutch terms shared a high correlation with the incidence of influenza-like illness  [10]. Regarding the Korean search engine, Daum, a study found 13 combined queries with r values ≥0. 7  [22]. In Italy, a study found 5 entry terms with high correlation coefficients >0. 6  [23]. Therefore, the related research on influenza prediction and the future improvement of influenza surveillance systems can make the above categories of relevant search terms in China as a reference.
Cross-references can be made for different countries and language systems; however, individualized analyses should be performed. Increasing the use of big data is not always beneficial. Not all relevant search terms positively affect the accuracy and timeliness of influenza prediction. Inconsistent with previous research, this study demonstrated that increasing the use of Baidu search terms reduces the accuracy and time before influenza prediction. The same conclusion has been confirmed in GFT-related studies, which indicated that GFT data might not provide reliable surveillance for seasonal or pandemic influenza  [24]. This may be because climate and strain, which are important factors affecting influenza, were not considered. Existing studies have shown that the contribution of the Baidu search index to influenza prediction was much lower than that of climate when climate and Baidu search indexes were used to construct a prediction model of influenza  [25].
This procedure led us to remove one chain of five participants in the Speed-Only condition, which we replaced with a new chain of five participants. No chain was removed for the Configurations+Speed+Noise condition. In total, the data of six participants were excluded, giving us a final sample of 140 participants (14 chains of five participants for each condition). Wheel speed corresponded to the best speed of the first or last two trials. Wheels that did not travel down were assigned a speed of 0 m h -1. To compute the intra-generation similarity score, we first selected the configuration of the wheel that was fastest among the last two trials of each participant. Then, for the within-condition similarity score, we compared this best configuration with the 13 best configurations produced by the other participants of the same generation, and we did so for each participant of each generation.
Wheel speed corresponded here to the best speed of the last two trials. Wheels that did not travel down were assigned a speed of 0 m h -1. We used regression modelling in R [(69); lmerTest package  (70) ] to fit a linear model with 'Wheel speed' as outcome variable, 'Generation' as fixed effect, and 'Chain's identity' as random effect. The same analyses were conducted for the understanding scores (Total, Center of Mass, and Inertia), the intra-generation similarity score (Within-condition and Between-condition), the exploration scores (Center of Mass and Inertia) and the best speed of the first two trials (Two participants in the Speed-Only condition and four participants in the Configurations+Speed+Noise condition were excluded from this analysis because their first two wheels did not descend).
We also used regression modelling in R [(69); lmerTest package  (70) ] to explore the links between the understanding scores (Total) and the best speed of the first or last two trials. We fitted a linear mixed model with 'Understand Score (Total)' as outcome variable, 'Wheel speed (the best speed of the first or last two trials)' as fixed effect, and 'Generation' and 'Chain's identity' as random effects. Statistical significance was set at p <. 05 and bootstrapping method was used to estimate 95% confidence intervals.
Funding This work was supported by grants from the  French National Research Agency (ANR)  project  TECHNITION  ( ANR-21-CE28-0023-01 ; FO, NC, NU and ER) and was performed within the framework of the  LA E C RTE of Universit de Lyon  ( ANR-11-LABX-0042 ; FO and ER), within the program " Investissements d'Avenir"  ( ANR-11-IDEX-0007 ; FO and ER) operated by  ANR. NU was supported by the  Max Planck Society  and a grant from the  Templeton World Charity Foundation  (https://www. templetonworldcharity. org/) # 0271. The opinions expressed in this publication are those of the authors and do not necessarily reflect the views of Templeton  World Charity Foundation, Inc.
Background The randomized phase 3 COMBI-i trial did not meet its primary endpoint of improved progressionfree survival (PFS) with spartalizumab plus dabrafenib and trametinib (sparta-DabTram) vs placebo plus dabrafenib and trametinib (placebo-DabTram) in the overall population of patients with unresectable/metastatic BRAF V600-mutant melanoma. This prespecified exploratory biomarker analysis was performed to identify subgroups that may derive greater treatment benefit from sparta-DabTram. Methods In COMBI-i ( ClinicalTrials. gov, NCT02967692), 532 patients received spartalizumab 400 mg intravenously every 4 weeks plus dabrafenib 150 mg orally two times daily and trametinib 2 mg orally one time daily or placebo-DabTram. Baseline/on-treatment pharmacodynamic markers were assessed via flow cytometry-based immunophenotyping and plasma cytokine profiling.
BACKGROUND 7] [8]  Checkpoint inhibitor plus targeted therapy combinations vs targeted therapy alone have since been investigated in randomized phase 2 (KEYNOTE-022, NCT02130466)  9  and phase 3 studies (IMspire150, NCT02908672; COMBI-i, NCT02967692)  10 11  in patients with BRAF V600 mutation-positive unresectable or metastatic melanoma. ] [11]  Exploratory findings from COMBI-i parts 1 and 2 demonstrated on-treatment biomarker modulations, including increased expression of T-cell-inflamed signatures (TIS) and decreased mitogen-activated protein kinase (MAPK) pathway activity, in samples from patients treated with combination of the anti-programmed death receptor 1 (PD-1) monoclonal antibody spartalizumab, the BRAF inhibitor dabrafenib, and the MEK inhibitor trametinib (sparta-DabTram). The objective response rate with sparta-DabTram was 78% (28 of 36 patients), with 16 patients (44%) achieving a complete response.
Study design The global, phase 3 COMBI-i study (NCT02967692) includes a safety run-in (part 1), biomarker cohort (part 2), and randomized, double-blind, placebo-controlled part 3. Enrollment in part 3 occurred from September 13, 2017, to July 4, 2018, at 179 centers in 29 countries, with a total of 532 patients randomized 1:1 to receive the recommended phase 3 regimen of intravenous spartalizumab 400 mg or placebo every 4 weeks in combination with the approved doses of oral dabrafenib 150 mg two times daily and oral trametinib 2 mg one time daily. 12 A random permuted block scheme and interactive response technology facilitated assignment of patient numbers to randomization numbers by investigators or study site staff. Patients, investigators, and study site staff remained blind to treatment identity from randomization until the primary analysis database lock.
Statistical analysis The data cut-off for these analyses was July 1, 2020 (median follow-up, 27. 2 months (IQR 25. 4-29. 0 months)). Contributions of biomarkers and covariates to PFS and OS were estimated using Cox proportional hazards models, univariate or multivariable as appropriate. Betweengroup comparisons were assessed by Wald or Wilcoxon rank sum tests with descriptive p values unadjusted for multiple comparisons. Biomarkers of potential predictive value were further assessed in multivariate analyses to determine statistical significance of the treatment interaction, and evidence for biomarker effects after adjusting for other factors was assessed using likelihood ratio tests with Cox models. All biomarker analyses were performed using R 3. 6. 1 and Bioconductor 3. 9. Kaplan-Meier curves and Cox proportional hazards models for biomarker cohorts were generated using the R survival (3.