{"id":"W2786629122","doi":"","title":"Développement de méthodes d’analyse fréquentielle non-stationnaire avec l’approche des dépassements de seuil et application avec la précipitation totale journalière dans le sud-est du Canada.","year":2017,"lang":"fr","type":"article","venue":"EspaceINRS Institutional Digital Repository (Institut National de la Recherche Scientifique)","topic":"Hydrology and Drought Analysis","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Humanities; Philosophy","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","sts","scholarly_communication"],"consensus_categories":["sts"],"category_scores_codex":[0.008095545,0.0005083006,0.0003731142,0.0002442412,0.004201538,0.001833922,0.00106873,0.0007476611,0.00007945128],"category_scores_gemma":[0.005511914,0.0005711938,0.0003043204,0.0006481807,0.003658359,0.003281266,0.0003716909,0.001050419,0.00007805908],"about_ca_system_candidate":true,"about_ca_system_consensus":true,"about_ca_system_score_codex":0.01145426,"about_ca_system_score_gemma":0.01271744,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.04571529,"about_ca_topic_score_gemma":0.1072706,"domain_scores_codex":[0.9938634,0.001952879,0.0008002623,0.0009929647,0.001580914,0.0008095839],"domain_scores_gemma":[0.996511,0.001078078,0.0007498328,0.0005677475,0.0005099113,0.0005834121],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","study_design_scores_codex":[0.0002406354,0.002478956,0.3361446,0.0004355569,0.001041683,0.0007910995,0.01932675,0.4260837,0.06161146,0.1231649,0.01444034,0.01424035],"study_design_scores_gemma":[0.002384913,0.00008151349,0.5394573,0.0007624741,0.0004845166,0.002088405,0.00156404,0.2206393,0.03851935,0.08661385,0.1057493,0.001655032],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5115144,0.0002789337,0.3506761,0.006830091,0.0005895597,0.0005087624,0.0001686649,0.00005388221,0.1293796],"genre_scores_gemma":[0.9359356,0.0002876699,0.03771294,0.0003083359,0.0002693953,0.0002064447,0.0002492646,0.00003500644,0.02499538],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4244212,"threshold_uncertainty_score":0.999674,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03975399600336716,"score_gpt":0.3133263842700991,"score_spread":0.2735723882667319,"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."}}