{"id":"W2906119240","doi":"","title":"Online predictive diagnosis of electrical train door systems","year":2013,"lang":"fr","type":"preprint","venue":"HAL (Le Centre pour la Communication Scientifique Directe)","topic":"Railway Engineering and Dynamics","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"Bombardier (Canada)","funders":"","keywords":"Train; Probabilistic logic; Discriminative model; Computer science; Closing (real estate); Machine learning; Artificial intelligence; Binary classification; Segmentation; Support vector machine","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.002585599,0.0006180009,0.0008764922,0.0003141325,0.0001424933,0.0002274144,0.001225118,0.0007076429,0.0001419076],"category_scores_gemma":[0.001518749,0.0007073251,0.0003685466,0.0006486651,0.0002464222,0.0001666049,0.0004718201,0.001244769,0.00006828703],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004452627,"about_ca_system_score_gemma":0.0002120776,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004022447,"about_ca_topic_score_gemma":0.0004126046,"domain_scores_codex":[0.994462,0.002482421,0.001102914,0.0007542096,0.0005463369,0.0006521427],"domain_scores_gemma":[0.9928243,0.002598924,0.0004450945,0.001883764,0.001890502,0.0003574408],"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":[0.00002304095,0.003345333,0.006735393,0.002669461,0.001084523,0.00001573508,0.01397868,0.5288423,0.003536842,0.3624225,0.004119541,0.07322663],"study_design_scores_gemma":[0.000505993,0.000001837003,0.009556571,0.003728978,0.0001431525,0.00001661042,0.0001227712,0.9691926,0.005649608,0.0007945932,0.009667668,0.0006196299],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3514647,0.0115163,0.613707,0.003087745,0.001425282,0.001212422,0.001358173,0.0007090973,0.01551924],"genre_scores_gemma":[0.916429,0.006027921,0.06532116,0.00001303408,0.0001235847,0.0004023151,0.001279934,0.0001844665,0.01021861],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.5649642,"threshold_uncertainty_score":0.9995378,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008887315817699061,"score_gpt":0.2020115906333959,"score_spread":0.1931242748156968,"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."}}