{"id":"W4417509101","doi":"10.1109/icsit65336.2025.11294664","title":"Automated Train Control Using Machine Vision for Red Flag Detection in Railway Safety","year":2025,"lang":"","type":"article","venue":"","topic":"IoT and GPS-based Vehicle Safety Systems","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"","keywords":"Flag (linear algebra); Scope (computer science); Hazard; Control (management); Work (physics); System safety; Machine vision; Damages","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002790888,0.0005345894,0.0003471012,0.0004828368,0.000264018,0.0004975091,0.0005512328,0.0006338789,0.001048622],"category_scores_gemma":[0.0004744738,0.0002317363,0.0004643605,0.0003786507,0.0002731732,0.0004931375,0.0004282805,0.0006650244,0.0005175449],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005794297,"about_ca_system_score_gemma":0.0007567555,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01424894,"about_ca_topic_score_gemma":0.01567948,"domain_scores_codex":[0.9997992,0.00002439202,0.000007026147,0.00007169698,0.00004217651,0.00005556711],"domain_scores_gemma":[0.9998944,0.00002385039,0.00001392269,0.00001611842,0.00004153002,0.00001025131],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0003196978,0.0003887812,0.00614573,0.0001629092,0.00008004092,0.0002392643,0.0001299891,0.4107114,0.06910299,0.001770676,0.006075025,0.5048735],"study_design_scores_gemma":[0.000006069182,0.00008022005,0.002442588,0.00001083304,0.00001315555,0.00003059489,0.00002364652,0.983491,0.01200709,0.0005219959,0.001362829,0.000009910262],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3813496,0.001402905,0.6004554,0.0004496419,0.0003000716,0.0001204834,0.0005081926,0.006648346,0.008765358],"genre_scores_gemma":[0.929336,0.0002891497,0.06569207,0.0001131475,0.00002995723,0.00003304535,0.000701125,0.00007422253,0.003731244],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01424894,"threshold_uncertainty_score":0.028332,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006980377613251591,"score_gpt":0.2576319377620392,"score_spread":0.2506515601487876,"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."}}