{"id":"W7005076461","doi":"","title":"Prédiction de trajectoires avec intégration contextuelle : exploitation du LLM BERT pour optimiser le covoiturage urbain dans des environnements chaotiques","year":2025,"lang":"fr","type":"other","venue":"Archipelago (University of Quebec in Montreal)","topic":"Marine Sponges and Natural Products","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Taxis; Air transport; Roll call; Service (business)","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004932675,0.001676522,0.0008912592,0.001710081,0.0005009457,0.001596458,0.0009390031,0.0012129,0.003069141],"category_scores_gemma":[0.001701795,0.0005630358,0.001011639,0.001613297,0.0003364111,0.001207431,0.0009897678,0.0011201,0.00108859],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001003924,"about_ca_system_score_gemma":0.001538307,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.05243664,"about_ca_topic_score_gemma":0.06409249,"domain_scores_codex":[0.9997452,0.00004010837,0.00001243578,0.00007874706,0.00006952075,0.00005411685],"domain_scores_gemma":[0.9995584,0.0001561571,0.00004776082,0.00003875425,0.0001439141,0.00005501952],"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":[0.0003372952,0.0001744454,0.02208686,0.0003119539,0.000108226,0.0002928159,0.0002396654,0.8788818,0.005545271,0.001479686,0.003480694,0.08706131],"study_design_scores_gemma":[0.000009503487,0.00003252624,0.002992138,0.00001930008,0.00001899723,0.00001580023,0.00008910318,0.9941261,0.0008675474,0.0005103785,0.001302691,0.00001601226],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"methods","genre_scores_codex":[0.5214531,0.00326392,0.4449259,0.001224143,0.0004842865,0.0002928324,0.006322376,0.006805435,0.01522799],"genre_scores_gemma":[0.9076023,0.000738538,0.08282977,0.00008385826,0.0000517546,0.0001470124,0.003145093,0.000196043,0.005205716],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.05243664,"threshold_uncertainty_score":0.1042629,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.004830467658490711,"score_gpt":0.1857120170943733,"score_spread":0.1808815494358826,"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."}}