{"id":"W7048689359","doi":"","title":"Modélisation des interactions entre les modes de transport par l'intégration de sources diversifiées de données","year":2023,"lang":"fr","type":"other","venue":"PolyPublie (École Polytechnique de Montréal)","topic":"Water Quality Monitoring and Analysis","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Fonds de recherche du Québec – Nature et technologies; Institut de Valorisation des Données; Ministère des Transports; Canada Research Chairs","keywords":"Homogeneous; Context (archaeology); Transport system","routes":{"ca_aff":false,"ca_fund":true,"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","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.001007116,0.0006565884,0.000557627,0.0005316717,0.0009890937,0.0002930362,0.000676622,0.0006561339,0.00156011],"category_scores_gemma":[0.0001494299,0.0006976758,0.0005018255,0.0006260697,0.000582766,0.0006089355,0.0001585931,0.0006825045,0.0002076293],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003700905,"about_ca_system_score_gemma":0.0001731221,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.2913806,"about_ca_topic_score_gemma":0.0891715,"domain_scores_codex":[0.9963444,0.0005181721,0.0006420632,0.0007802206,0.0005117017,0.00120338],"domain_scores_gemma":[0.9982049,0.0002356237,0.0004167091,0.0006024767,0.00004245579,0.00049782],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0000695429,0.0004183611,0.6664492,0.000189466,0.0003511877,0.00007342371,0.00862849,0.2239227,0.05642911,0.001472864,0.001461565,0.04053405],"study_design_scores_gemma":[0.000572397,0.0001723798,0.4924192,0.001295852,0.001138492,0.0001123322,0.006261333,0.3349278,0.1451816,0.007512038,0.008707666,0.001698902],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6045849,0.0006602602,0.389186,0.002049455,0.0001424175,0.0004192868,0.0002002579,0.001006478,0.001750956],"genre_scores_gemma":[0.934269,0.001982417,0.02617191,0.0001195619,0.0004966689,0.000332366,0.00008954969,0.0003109553,0.03622763],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3630141,"threshold_uncertainty_score":0.9995474,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02180117483555427,"score_gpt":0.2362972044911174,"score_spread":0.2144960296555632,"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."}}