{"id":"W4399514622","doi":"10.1038/s41597-024-03395-w","title":"A Global Multi-Source Tropical Cyclone Precipitation (MSTCP) Dataset","year":2024,"lang":"en","type":"article","venue":"Scientific Data","topic":"Tropical and Extratropical Cyclones Research","field":"Earth and Planetary Sciences","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université du Québec à Montréal","funders":"Natural Sciences and Engineering Research Council of Canada; Nuclear Safety and Security Commission; Université du Québec à Montréal; National Aeronautics and Space Administration","keywords":"Tropical cyclone; Precipitation; Storm; Environmental science; Climatology; Tropical cyclone scales; Quantitative precipitation estimation; Meteorology; Cyclone (programming language); Geography; Geology; Computer science","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0005189345,0.000845154,0.0006510185,0.001922242,0.0003751994,0.0007310389,0.001178399,0.0007464916,0.0127025],"category_scores_gemma":[0.002017625,0.0002125896,0.0006548474,0.00388759,0.0001614089,0.0006372386,0.0008107291,0.0008959374,0.01262154],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007807342,"about_ca_system_score_gemma":0.001256468,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02080631,"about_ca_topic_score_gemma":0.02533638,"domain_scores_codex":[0.9994943,0.00006781011,0.00008615125,0.0001372341,0.0001443091,0.00007016884],"domain_scores_gemma":[0.9988907,0.0001699016,0.0001448194,0.0002109318,0.0004907899,0.00009285305],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0001811721,0.0000994776,0.01505977,0.0007044475,0.0001200579,0.00014501,0.00007340511,0.004617812,0.001299766,0.001111407,0.9630352,0.01355239],"study_design_scores_gemma":[0.0003113244,0.00006188168,0.07356267,0.0002553677,0.00006455342,0.0001587679,0.0002424969,0.009317192,0.002238594,0.001356024,0.9123622,0.0000690136],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.001727267,0.00003581243,0.0001888431,0.00004654427,0.0000225942,0.00002640489,0.9970523,0.0002271163,0.0006730991],"genre_scores_gemma":[0.002296754,0.00002747538,0.0005013011,0.00001788048,0.000008980512,0.00006294507,0.9968238,0.00002696267,0.0002339181],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.02080631,"threshold_uncertainty_score":0.04249406,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07639561662460964,"score_gpt":0.3323827925702518,"score_spread":0.2559871759456422,"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."}}