{"id":"W3137525530","doi":"10.5296/jas.v9i2.18274","title":"Gold Standard Agreement Model for Precipitation Forecast in Paraná Using Bootstrap","year":2021,"lang":"en","type":"article","venue":"Journal of Agricultural Studies","topic":"Environmental and biological studies","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Universidade Tecnológica Federal do Paraná; Conselho Nacional de Desenvolvimento Científico e Tecnológico; Coordenação de Aperfeiçoamento de Pessoal de Nível Superior","keywords":"Environmental science; Atmospheric research; Climatology; Standard deviation; Meteorology; Quantitative precipitation forecast; Bootstrapping (finance); Precipitation; Forecast verification; Forecast skill; Geography; Statistics; Mathematics; Econometrics","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001942466,0.0001267778,0.0002839992,0.00001043831,0.0001081382,0.0000147117,0.00007860387,0.00003511693,0.00002768902],"category_scores_gemma":[0.00006926119,0.00006393326,0.0001305109,0.000111413,0.0001010449,0.0001807598,0.0001372002,0.00008490144,0.000003111394],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003884219,"about_ca_system_score_gemma":0.000003841573,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000006948194,"about_ca_topic_score_gemma":0.0002283559,"domain_scores_codex":[0.9989983,0.00002997935,0.0003787606,0.0001428295,0.0002560846,0.0001940127],"domain_scores_gemma":[0.9996169,0.00006422079,0.0002023803,0.00003372875,0.00004148136,0.00004131814],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","study_design_scores_codex":[0.0006036085,0.0008192143,0.3253437,0.0001215861,0.0007979317,0.00007092767,0.007755125,0.3621829,0.2567621,0.0001362401,0.01786723,0.02753934],"study_design_scores_gemma":[0.001753781,0.0008795489,0.9659829,0.0001762856,0.0001320601,0.0000722758,0.01481599,0.001573965,0.009842261,0.002847129,0.001531527,0.0003923418],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9960208,0.002280149,0.0003498511,0.000850648,0.0001047763,0.0001485819,0.00001189051,0.000002657112,0.0002306637],"genre_scores_gemma":[0.9912804,0.001489367,0.006660792,0.00008794714,0.00006745606,0.000008402301,0.00000222637,0.000002486717,0.0004009272],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6406391,"threshold_uncertainty_score":0.2607123,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.103635438633687,"score_gpt":0.3044552034512273,"score_spread":0.2008197648175402,"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."}}