{"id":"W4389205236","doi":"10.5194/essd-2023-460","title":"A Global Multi-Source Tropical Cyclone Precipitation (MSTCP) Dataset","year":2023,"lang":"en","type":"preprint","venue":"","topic":"Tropical and Extratropical Cyclones Research","field":"Earth and Planetary Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","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; Environmental science; Storm; Tropical cyclone scales; Climatology; Meteorology; Satellite; Quantitative precipitation estimation; Global Precipitation Measurement; Flash flood; Flood myth; Cyclone (programming language); Computer science; Geography; Geology","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005889258,0.0008951537,0.0007106419,0.00189401,0.0003975764,0.001005908,0.001475985,0.0008772146,0.01565903],"category_scores_gemma":[0.002543824,0.0003236527,0.0007067039,0.005451676,0.0001789092,0.0009575256,0.001008771,0.001074267,0.01795066],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007194863,"about_ca_system_score_gemma":0.00137336,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02760035,"about_ca_topic_score_gemma":0.03150601,"domain_scores_codex":[0.9994279,0.0000762239,0.00009933955,0.0001653948,0.0001507379,0.00008046857],"domain_scores_gemma":[0.9986398,0.0002110635,0.000209696,0.0002679905,0.0005536317,0.0001178936],"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":[0.00009612618,0.00003582175,0.007902284,0.0005581517,0.00008451935,0.00006508429,0.00005096019,0.001558129,0.0004635228,0.0006441766,0.9821038,0.006437486],"study_design_scores_gemma":[0.0003562775,0.00003360816,0.05994877,0.0003093875,0.00006404868,0.0001008135,0.000174876,0.00509463,0.001290417,0.001024556,0.9315404,0.00006228304],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0006610702,0.00002694339,0.00009145679,0.00003430212,0.00001479241,0.00001113429,0.9985788,0.0001921491,0.0003893765],"genre_scores_gemma":[0.00124806,0.000024927,0.0003434172,0.00001506184,0.000007520411,0.00004350823,0.9980983,0.0000359365,0.0001833297],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.02760035,"threshold_uncertainty_score":0.05487937,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0758878819795193,"score_gpt":0.3219741237790502,"score_spread":0.2460862417995309,"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."}}