{"id":"W2163899350","doi":"10.1029/2003wr002295","title":"On the objective identification of flood seasons","year":2004,"lang":"en","type":"article","venue":"Water Resources Research","topic":"Hydrology and Drought Analysis","field":"Environmental Science","cited_by":87,"is_retracted":false,"has_abstract":true,"ca_institutions":"Institut National de la Recherche Scientifique","funders":"","keywords":"Flood myth; 100-year flood; Seasonality; Hydrology (agriculture); Environmental science; Identification (biology); Flood forecasting; Sampling (signal processing); Distribution (mathematics); Statistics; Geography; Computer science; Mathematics; Geology; Ecology","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":[0.003152049,0.0005421824,0.0006403066,0.002307447,0.0003078458,0.001234957,0.0005538588,0.0006401672,0.001642273],"category_scores_gemma":[0.01373406,0.0002833499,0.0003121631,0.0009384727,0.0007831014,0.001376727,0.0011707,0.0004799642,0.0007390206],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002533027,"about_ca_system_score_gemma":0.0005055481,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001313718,"about_ca_topic_score_gemma":0.002948828,"domain_scores_codex":[0.9983773,0.0006033101,0.0001345404,0.0003828565,0.0004350592,0.00006702953],"domain_scores_gemma":[0.9862255,0.009255104,0.00195038,0.0009262445,0.001439565,0.0002032283],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.0007067298,0.0002767571,0.0813062,0.0008718741,0.0001615927,0.0002291661,0.001201827,0.02958809,0.04635533,0.005936897,0.002473854,0.8308917],"study_design_scores_gemma":[0.0002025771,0.001178414,0.3709504,0.0003466005,0.0001750279,0.001737728,0.002177344,0.5212321,0.06159635,0.02181217,0.01813557,0.0004556675],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1379618,0.0003114222,0.8571645,0.00006849935,0.00004673513,0.0002219248,0.0006856801,0.0006164277,0.002922962],"genre_scores_gemma":[0.581255,0.0005352948,0.4121884,0.00009801929,0.0001130025,0.0004593281,0.001441236,0.0001559678,0.003753775],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003152049,"threshold_uncertainty_score":0.01666981,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02458793240802083,"score_gpt":0.2966253821565822,"score_spread":0.2720374497485614,"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."}}