{"id":"W6930881415","doi":"10.5281/zenodo.15550286","title":"Dataset for the paper : Prediction and uncertainty quantification of drought in North Benin","year":2025,"lang":"en","type":"dataset","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Celiac Disease Research and Management","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université du Québec à Trois-Rivières","funders":"","keywords":"Uncertainty quantification; Uncertainty analysis; Time series; Measurement uncertainty; Propagation of uncertainty; Sensitivity analysis","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007395543,0.001347748,0.000870822,0.001476964,0.0006942172,0.0009081798,0.001755139,0.001912632,0.02373246],"category_scores_gemma":[0.003011174,0.0002633159,0.0008660842,0.002393913,0.0003051787,0.000550257,0.0008919159,0.0009505149,0.01181371],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0012533,"about_ca_system_score_gemma":0.001366743,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03890565,"about_ca_topic_score_gemma":0.06246115,"domain_scores_codex":[0.9996126,0.00009765197,0.00004609305,0.0001006179,0.00007977346,0.0000633493],"domain_scores_gemma":[0.9988981,0.0004616144,0.00009601261,0.0001521163,0.0002835191,0.0001086007],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"observational","study_design_scores_codex":[0.0002101555,0.0001031449,0.004864783,0.00101823,0.00009489413,0.0001068299,0.00004835425,0.003765713,0.0002081803,0.0005360274,0.9822795,0.006764132],"study_design_scores_gemma":[0.001222405,0.000110257,0.04545378,0.0008237705,0.0001428723,0.0003047204,0.0004882667,0.01262382,0.001016127,0.003348763,0.9343544,0.0001107277],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.001889331,0.0002064248,0.000123156,0.0002711134,0.00006158244,0.00002823944,0.9965551,0.0002336025,0.0006313814],"genre_scores_gemma":[0.004782328,0.00009246056,0.0004701867,0.00008461556,0.00001795112,0.0001272438,0.9937904,0.00002511167,0.0006095924],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.03890565,"threshold_uncertainty_score":0.07939297,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03041999640841008,"score_gpt":0.2818563129919171,"score_spread":0.251436316583507,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}