{"id":"W4313897257","doi":"10.4236/jtts.2023.131001","title":"Failure Prediction and Intelligent Maintenance of a Transportation Company’s Urban Fleet","year":2023,"lang":"en","type":"article","venue":"Journal of Transportation Technologies","topic":"Machine Fault Diagnosis Techniques","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"École de Technologie Supérieure; Université du Québec à Montréal","funders":"","keywords":"Work (physics); Set (abstract data type); Order (exchange); Intelligent transportation system; Code (set theory); Fleet management; Computer science; Transport engineering; Operations research; Engineering; Business","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002058811,0.0001412112,0.0002880227,0.0005165333,0.00002507829,0.00001102117,0.0001708234,0.0001517002,0.000006604057],"category_scores_gemma":[0.00003295439,0.0001269123,0.00009181847,0.0004914604,0.00008528581,0.0002576248,0.000001733238,0.0002626447,0.000001033615],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000334509,"about_ca_system_score_gemma":0.00001233464,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000006784384,"about_ca_topic_score_gemma":0.00008924628,"domain_scores_codex":[0.9988213,0.000008674142,0.0006960145,0.0001002686,0.0002307296,0.000143073],"domain_scores_gemma":[0.9994161,0.00005418044,0.0002360909,0.0001206474,0.0001458411,0.00002712815],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0002979774,0.0003794689,0.4329132,0.003504588,0.0009210021,0.0002708284,0.01314289,0.07971983,0.1525327,0.01462756,0.1307008,0.1709892],"study_design_scores_gemma":[0.001021227,0.0006347543,0.4776926,0.0009726043,0.000212087,0.00002317738,0.006515761,0.007511893,0.4840593,0.006072261,0.01487045,0.0004139255],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9745188,0.0005252185,0.02236939,0.0005268834,0.0001469857,0.0001975603,0.0002012671,0.001487689,0.00002615978],"genre_scores_gemma":[0.9922982,0.002864609,0.004715265,0.000004740744,0.00001495658,0.00001799982,0.0000572577,0.00002120341,0.000005794921],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3315266,"threshold_uncertainty_score":0.5175334,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009609880599143346,"score_gpt":0.2437861184124998,"score_spread":0.2341762378133564,"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."}}