{"id":"W7033006257","doi":"","title":"Optimisation de la prévision hydrologique d'ensemble par
\\nl'intégration de l'apprentissage profond dans une méthode
\\nmulti-modèle hybride","year":2024,"lang":"fr","type":"other","venue":"Espace École de technologie supérieure (École de technologie supérieure)","topic":"Science, Technology, and Education in Latin America","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Drainage basin; Western europe; Bin","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaresearch","metaepi_narrow","sts","scholarly_communication","open_science","research_integrity","insufficient_payload"],"consensus_categories":["metaepi_narrow","sts","research_integrity","insufficient_payload"],"category_scores_codex":[0.007012108,0.002544556,0.0022431,0.003013099,0.001894279,0.001078644,0.00649049,0.01120916,0.001151184],"category_scores_gemma":[0.0133625,0.002682899,0.0009370856,0.009831266,0.01203893,0.0008572147,0.001851567,0.007448347,0.001056434],"about_ca_system_candidate":true,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00473059,"about_ca_system_score_gemma":0.004530197,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005026931,"about_ca_topic_score_gemma":0.01052727,"domain_scores_codex":[0.9850068,0.001828091,0.002086305,0.004183691,0.001646726,0.005248369],"domain_scores_gemma":[0.9909296,0.001800592,0.001915182,0.003966159,0.0005403564,0.0008480928],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0005628766,0.005024991,0.1411254,0.002025104,0.001337392,0.002765962,0.02993626,0.07517663,0.1260072,0.1066767,0.381327,0.1280345],"study_design_scores_gemma":[0.002690759,0.002544612,0.01481573,0.002501995,0.001416308,0.001388449,0.1206626,0.08334233,0.09285232,0.1570936,0.5138366,0.006854726],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.4073391,0.007101162,0.3765396,0.05188476,0.003777063,0.008288228,0.0003838996,0.029843,0.1148432],"genre_scores_gemma":[0.829924,0.004661122,0.07643224,0.0008528699,0.0006503774,0.003222922,0.0001404824,0.0008094064,0.08330652],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.422585,"threshold_uncertainty_score":0.9999583,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02067225657539396,"score_gpt":0.3077008625276708,"score_spread":0.2870286059522769,"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."}}