{"id":"W1778048511","doi":"10.1029/2005wr004245","title":"Switching the pooling similarity distances: Mahalanobis for Euclidean","year":2006,"lang":"en","type":"article","venue":"Water Resources Research","topic":"Hydrology and Drought Analysis","field":"Environmental Science","cited_by":56,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo; Conestoga College","funders":"","keywords":"Mahalanobis distance; Pooling; Statistics; Resampling; Mathematics; Flood myth; Euclidean distance; Confidence interval; Similarity (geometry); Econometrics; Computer science; Geography; Artificial intelligence","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":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.002544003,0.0001108876,0.0001386673,0.00006018528,0.001214888,0.0001348326,0.0005948783,0.00008490607,0.0009274289],"category_scores_gemma":[0.00006184508,0.0000585446,0.0001089656,0.0002541052,0.0003679977,0.0001189851,0.0003470826,0.0003631275,0.000422702],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009239766,"about_ca_system_score_gemma":0.000003285465,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002727991,"about_ca_topic_score_gemma":0.002930312,"domain_scores_codex":[0.9978353,0.0002975076,0.0002115301,0.0003718472,0.0005452698,0.0007385872],"domain_scores_gemma":[0.99919,0.0003235332,0.00002569552,0.0003796291,0.00001759138,0.00006356239],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0007663413,0.0005339559,0.7717932,0.0001164883,0.0002121969,0.0001263181,0.02512988,0.04035565,0.09919886,0.001138845,0.04169772,0.0189305],"study_design_scores_gemma":[0.000336648,0.00006726691,0.01067682,0.000009407211,0.000024969,0.000005362117,0.000399127,0.01245603,0.01150565,0.02498231,0.9393305,0.0002059268],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9726093,0.0001054957,0.001001115,0.003282947,0.0000247743,0.0002582827,0.00000536313,0.00003327244,0.02267947],"genre_scores_gemma":[0.9930588,0.000007709798,0.0002018476,0.0001507683,0.0001918034,0.00005870899,0.0000155847,0.00001520045,0.00629959],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8976328,"threshold_uncertainty_score":0.9999859,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02973884805520937,"score_gpt":0.3061779459281451,"score_spread":0.2764390978729357,"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."}}