{"id":"W7133270782","doi":"","title":"Évaluation de l’efficacité des technologies de vision pour l’inspection des wagons de chemin de fer","year":2024,"lang":"en","type":"other","venue":"Federal Open Science Repository of Canada / Le Dépôt fédéral de science ouverte du Canada","topic":"","field":"","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Transport Canada","keywords":"Reliability (semiconductor); Machine vision; Software; Key (lock); Quality (philosophy); Launched; Visual inspection","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002555441,0.0009367597,0.0004892027,0.002012105,0.0003760224,0.00147166,0.0007103813,0.001094564,0.003732974],"category_scores_gemma":[0.004757931,0.0003065667,0.0006824548,0.0008513027,0.0005118535,0.001272143,0.0006470119,0.0006852552,0.001280728],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002397397,"about_ca_system_score_gemma":0.001674175,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.05386271,"about_ca_topic_score_gemma":0.05224534,"domain_scores_codex":[0.9979312,0.000272316,0.00008299565,0.0003336228,0.001148001,0.0002317588],"domain_scores_gemma":[0.997362,0.0006241146,0.0001342097,0.0001959373,0.001563024,0.0001207918],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.002129823,0.001496246,0.03687593,0.001406454,0.000404173,0.0002468899,0.0003084337,0.1118511,0.1132287,0.002379053,0.008491653,0.7211816],"study_design_scores_gemma":[0.000251942,0.005442859,0.1297144,0.0004315116,0.0003977932,0.0004087984,0.0007946037,0.6459729,0.1928037,0.001106651,0.02251058,0.0001642965],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8800359,0.003629431,0.08463262,0.0004563596,0.0001990243,0.0005162355,0.001827201,0.00281685,0.02588635],"genre_scores_gemma":[0.924132,0.00112855,0.06456229,0.0001039705,0.0000192233,0.000121697,0.002389044,0.0001743773,0.007368943],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9461373,"threshold_uncertainty_score":0.1070983,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01509435904160904,"score_gpt":0.2633283795251636,"score_spread":0.2482340204835546,"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."}}