{"id":"W4409543050","doi":"10.32388/86ioz4","title":"Advancing Autonomous Vehicle Safety: A Combined Fault Tree Analysis and Bayesian Network Approach","year":2025,"lang":"en","type":"preprint","venue":"Qeios","topic":"Risk and Safety Analysis","field":"Decision Sciences","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Ottawa","funders":"Natural Sciences and Engineering Research Council of Canada; Mitacs","keywords":"Fault tree analysis; Bayesian network; Computer science; Bayesian probability; Tree (set theory); Fault (geology); Data mining; Reliability engineering; Engineering; Artificial intelligence; Mathematics","routes":{"ca_aff":true,"ca_fund":true,"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":[0.004022321,0.0005404992,0.002018153,0.001269832,0.0005553308,0.0005863689,0.001600612,0.0005266767,0.0002528098],"category_scores_gemma":[0.0009748103,0.0004447361,0.001147978,0.004751521,0.0001613202,0.0001789069,0.001786165,0.0008735969,0.00004227153],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001581016,"about_ca_system_score_gemma":0.0003235544,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006248203,"about_ca_topic_score_gemma":0.001920747,"domain_scores_codex":[0.9936318,0.0007322872,0.001656842,0.001898075,0.001393789,0.0006872358],"domain_scores_gemma":[0.9948089,0.001472126,0.0007646588,0.002275851,0.0003517381,0.0003267809],"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":[0.0002140702,0.0001521056,0.05545732,0.00004923035,0.002857556,0.00002530889,0.001100296,0.5915642,0.000006193293,0.00130776,0.004931599,0.3423344],"study_design_scores_gemma":[0.0005561441,0.00004058983,0.04289985,0.0000504262,0.002564893,0.000001742864,0.0005810587,0.896493,0.00001026891,0.04685574,0.009352147,0.0005941167],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01770164,0.002462973,0.9133105,0.005740169,0.000691735,0.000919217,0.0002383292,0.0002798189,0.05865565],"genre_scores_gemma":[0.9439262,0.00104868,0.03824453,0.0005782269,0.0003263115,0.00008520789,0.0002380212,0.0000305307,0.01552226],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9262246,"threshold_uncertainty_score":0.9998004,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02438397627483695,"score_gpt":0.3182124396105548,"score_spread":0.2938284633357178,"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."}}