{"id":"W4364382152","doi":"10.18280/ijsdp.180320","title":"Potential of the CHIRPS Database for Extreme Precipitation Risk Studies. Assessment in the State of Jalisco (Mexico)","year":2023,"lang":"en","type":"article","venue":"International Journal of Sustainable Development and Planning","topic":"Climate change impacts on agriculture","field":"Agricultural and Biological Sciences","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Consejo Nacional de Ciencia y Tecnología; Centre of Excellence for Environmental Decisions, Australian Research Council","keywords":"Precipitation; Environmental science; State (computer science); Database; Meteorology; Climatology; Geography; Geology; Computer science","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"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":[0.001369819,0.0003506438,0.0002691895,0.004067952,0.0003511324,0.001206758,0.0006962316,0.0003930467,0.001359042],"category_scores_gemma":[0.003409467,0.0001350347,0.0002439982,0.002887072,0.0001087272,0.0006819103,0.0006858423,0.00019368,0.0002253181],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001012343,"about_ca_system_score_gemma":0.001410433,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.05472567,"about_ca_topic_score_gemma":0.04219086,"domain_scores_codex":[0.999403,0.000187828,0.00007878846,0.0001217634,0.0001701225,0.00003852608],"domain_scores_gemma":[0.9973339,0.0006474665,0.0007371175,0.0003905762,0.0007532424,0.000137637],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.000588451,0.0002335733,0.7693026,0.0004165732,0.0003002415,0.0006249958,0.000382375,0.0309642,0.001645086,0.002276021,0.02115775,0.1721081],"study_design_scores_gemma":[0.00009519689,0.0001938373,0.8303919,0.0002623744,0.000243158,0.0003044964,0.001308282,0.1185512,0.003062997,0.001347908,0.04418625,0.00005249961],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8176045,0.0009404346,0.01489727,0.001035982,0.00006135411,0.0005319809,0.1442796,0.00210745,0.01854145],"genre_scores_gemma":[0.9171937,0.0005431827,0.01747515,0.000070459,0.00005015397,0.0004479153,0.06221654,0.00004082275,0.001962094],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.05472567,"threshold_uncertainty_score":0.1088143,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0652987471142041,"score_gpt":0.3207001963944207,"score_spread":0.2554014492802166,"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."}}