{"id":"W4407930231","doi":"10.1007/s41748-025-00592-4","title":"A New Composite Hydrological Response Anomalies Index in a Semi-arid Region Based on Random Forest Algorithm","year":2025,"lang":"en","type":"article","venue":"Earth Systems and Environment","topic":"Precipitation Measurement and Analysis","field":"Earth and Planetary Sciences","cited_by":22,"is_retracted":false,"has_abstract":false,"ca_institutions":"Université du Québec à Trois-Rivières; Innovation and Economic Development Trois Rivières","funders":"","keywords":"Arid; Random forest; Index (typography); Composite number; Composite index; Algorithm; Environmental science; Hydrology (agriculture); Geology; Mathematics; Computer science; Artificial intelligence; Geotechnical engineering; Composite indicator; Econometrics","routes":{"ca_aff":true,"ca_fund":false,"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":[0.0007296191,0.0004455819,0.0006928814,0.002136261,0.0003102024,0.0006398527,0.0007523033,0.000382011,0.0008688912],"category_scores_gemma":[0.0008565052,0.0001782193,0.0005197884,0.001572902,0.0001678154,0.0008529698,0.000365085,0.0003379374,0.0002816085],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003799548,"about_ca_system_score_gemma":0.0007527318,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008693441,"about_ca_topic_score_gemma":0.009226986,"domain_scores_codex":[0.9996117,0.00006944899,0.0000262757,0.0001189563,0.0001237459,0.00004987789],"domain_scores_gemma":[0.9996077,0.00008547553,0.0000508339,0.00002207085,0.0001988998,0.00003500977],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0008356066,0.0004541007,0.05683051,0.0001834506,0.0004021299,0.0001768896,0.00005584096,0.2960104,0.02160729,0.003582191,0.009583166,0.6102784],"study_design_scores_gemma":[0.00003845023,0.00003839995,0.01219339,0.000004654868,0.00004777456,0.00005469222,0.00001416977,0.9845691,0.001544295,0.0006508901,0.00082308,0.00002101721],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2906402,0.0007366838,0.7023771,0.0001736461,0.0001424296,0.0001467852,0.001615969,0.00188953,0.00227763],"genre_scores_gemma":[0.6783422,0.0002524056,0.3154722,0.00005590105,0.0001119792,0.0001568609,0.003523827,0.0001166186,0.001967956],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.008693441,"threshold_uncertainty_score":0.01728565,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01206818938689187,"score_gpt":0.1886584702704701,"score_spread":0.1765902808835782,"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."}}