{"id":"W4379193840","doi":"10.1016/j.dib.2023.109294","title":"A new long term gridded daily precipitation dataset at high-resolution for Cuba (CubaPrec1)","year":2023,"lang":"en","type":"article","venue":"Data in Brief","topic":"Precipitation Measurement and Analysis","field":"Earth and Planetary Sciences","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Agencia Estatal de Investigación; Ministerio de Ciencia, Tecnología y Medio Ambiente; European Commission; International Development Research Centre","keywords":"Precipitation; Environmental science; Grid; Climatology; Meteorology; Term (time); High resolution; Spatial coherence; Remote sensing; Coherence (philosophical gambling strategy); Geography; Geology; Statistics; Geodesy; Mathematics","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":[0.0006887164,0.0006994028,0.0008105554,0.00220671,0.0005183,0.000871729,0.001334182,0.0003343296,0.006007097],"category_scores_gemma":[0.001870512,0.0003308853,0.0004746852,0.005274887,0.0001488934,0.0005967518,0.001036696,0.0009731886,0.002902832],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001910016,"about_ca_system_score_gemma":0.003172518,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.2319796,"about_ca_topic_score_gemma":0.2001404,"domain_scores_codex":[0.9994715,0.0001028999,0.00006092992,0.0001415936,0.0001308945,0.00009220029],"domain_scores_gemma":[0.9981498,0.0001147536,0.0002684431,0.0002898672,0.001016077,0.0001612197],"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":[0.0010854,0.0002571239,0.05997732,0.001314296,0.0004132944,0.0003671573,0.0004289424,0.009916722,0.004094877,0.002710896,0.8634318,0.05600221],"study_design_scores_gemma":[0.0003242248,0.00003949565,0.2719724,0.0002968347,0.0001022718,0.0001588357,0.0005512931,0.01107932,0.002632855,0.0008414445,0.7118928,0.0001082413],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.01580409,0.0002213913,0.001317515,0.0001590996,0.00006006041,0.0001226403,0.9799665,0.0004653914,0.001883356],"genre_scores_gemma":[0.01370318,0.0001031472,0.003183024,0.00002579421,0.00001586464,0.0003882658,0.9815403,0.0001402255,0.0009002116],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.2319796,"threshold_uncertainty_score":0.4612586,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07156285292008607,"score_gpt":0.2873616682714939,"score_spread":0.2157988153514078,"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."}}