{"id":"W1954513632","doi":"10.1002/2013wr013523","title":"Depth-based regional index-flood model","year":2013,"lang":"en","type":"article","venue":"Water Resources Research","topic":"Hydrology and Drought Analysis","field":"Environmental Science","cited_by":23,"is_retracted":false,"has_abstract":true,"ca_institutions":"Institut National de la Recherche Scientifique","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs","keywords":"Weighting; Flood myth; Similarity (geometry); Statistics; Mean squared error; Computer science; Function (biology); Identification (biology); Mathematics; Data mining; Mathematical optimization; Algorithm; Geography; Artificial intelligence","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0008135943,0.0001188805,0.0001359548,0.0001538086,0.0003899374,0.00008275971,0.0005580226,0.0001443374,0.01202615],"category_scores_gemma":[0.00002511418,0.00007773501,0.00008423732,0.0003105447,0.0006922893,0.0001853386,0.0003644628,0.000427809,0.01411238],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008117058,"about_ca_system_score_gemma":0.000008909708,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002815567,"about_ca_topic_score_gemma":0.0004612396,"domain_scores_codex":[0.9974837,0.0002751607,0.0001744903,0.0004159276,0.0009006163,0.0007501631],"domain_scores_gemma":[0.9992459,0.00006984052,0.00001651097,0.0004353321,0.00002764287,0.0002047836],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001693607,0.0004402026,0.6273392,0.00002008385,0.00008381569,0.00005028392,0.004879971,0.2635463,0.03611123,0.00007951374,0.06118822,0.006091816],"study_design_scores_gemma":[0.0006175955,0.0001070672,0.02732954,0.000007307599,0.00001152268,0.000005379613,0.000111557,0.842914,0.008977766,0.007764181,0.1118606,0.0002935267],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9474699,0.00001997144,0.0004838865,0.00427259,0.000007617901,0.0001750121,8.104553e-7,0.00003690691,0.04753334],"genre_scores_gemma":[0.9838693,0.000003172845,0.0003839358,0.0005455437,0.00004353197,0.00009797217,0.00001138896,0.00001594096,0.01502918],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6000097,"threshold_uncertainty_score":0.988877,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04688977074889458,"score_gpt":0.2988096075183038,"score_spread":0.2519198367694093,"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."}}