{"id":"W2737088591","doi":"10.1016/j.jhydrol.2017.07.052","title":"A systematic approach to selecting the best probability models for annual maximum rainfalls – A case study using data in Ontario (Canada)","year":2017,"lang":"en","type":"article","venue":"Journal of Hydrology","topic":"Hydrology and Drought Analysis","field":"Environmental Science","cited_by":52,"is_retracted":false,"has_abstract":false,"ca_institutions":"McGill University","funders":"Natural Sciences and Engineering Research Council of Canada; Faculty of Engineering, McGill University","keywords":"Statistics; Probability distribution; Goodness of fit; Environmental science; Mathematics; Computer science","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004258876,0.0001408855,0.000537687,0.00005928511,0.0005708964,0.00004553961,0.001205196,0.00007908604,0.00001980362],"category_scores_gemma":[0.0004643306,0.00009296223,0.00006729724,0.00009669618,0.0001207623,0.0004764934,0.0005314031,0.0003835296,0.000001343284],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004813674,"about_ca_system_score_gemma":0.0002888706,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.789171,"about_ca_topic_score_gemma":0.9898425,"domain_scores_codex":[0.9979939,0.0003745997,0.0006923584,0.0003200669,0.0002836614,0.0003354482],"domain_scores_gemma":[0.9980206,0.000235356,0.0006646535,0.0009452488,0.00003428153,0.00009984049],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002467877,0.001021679,0.367145,0.000167323,0.0003986785,0.001428231,0.01317699,0.6160198,0.00005932183,0.00001315182,0.0002275291,0.00009543391],"study_design_scores_gemma":[0.001932502,0.0009872076,0.006690471,0.00009929828,0.0009617622,0.0132316,0.007566318,0.9637784,0.000005892004,0.004335373,0.00007854506,0.0003325833],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9933002,0.00001614027,0.004763417,0.0004921568,0.00008889424,0.0009443425,0.000005739289,0.000001752933,0.0003873427],"genre_scores_gemma":[0.9963433,2.506481e-7,0.003370744,0.0001779618,0.00003666845,0.00002080777,6.847669e-7,0.000007783804,0.0000418159],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3604546,"threshold_uncertainty_score":0.4390931,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06782532937238565,"score_gpt":0.2918156529587537,"score_spread":0.2239903235863681,"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."}}