{"id":"W2034651011","doi":"10.1007/s00170-012-4391-x","title":"Condition monitoring for the endurance test of automotive light assemblies","year":2012,"lang":"en","type":"article","venue":"The International Journal of Advanced Manufacturing Technology","topic":"Machine Fault Diagnosis Techniques","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":false,"ca_institutions":"Queen's University; University of Calgary","funders":"","keywords":"Support vector machine; Pattern recognition (psychology); Wavelet; Structural risk minimization; Feature extraction; Vibration; Feature vector; Classifier (UML); Condition monitoring; Automotive industry; Time domain; Artificial intelligence; Computer science; Engineering; Acoustics; Computer vision","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":[0.0002942788,0.0003314356,0.0003176317,0.000748352,0.0003419689,0.0003647928,0.0004947741,0.0004839086,0.001975186],"category_scores_gemma":[0.001062978,0.0001818936,0.0001557971,0.0002393712,0.000204769,0.0005233772,0.0003100153,0.0003088838,0.0002935295],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001875896,"about_ca_system_score_gemma":0.0001619908,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004169768,"about_ca_topic_score_gemma":0.0006258058,"domain_scores_codex":[0.999742,0.00004092779,0.00001416041,0.00005953357,0.0001068115,0.00003657323],"domain_scores_gemma":[0.9992701,0.0003107723,0.0001547254,0.0000708698,0.0001388604,0.000054613],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00175839,0.0001375902,0.01190178,0.0001188611,0.00002264594,0.0002692434,0.0001922168,0.002794294,0.887737,0.0002511332,0.0004204759,0.09439629],"study_design_scores_gemma":[0.0001003049,0.002864566,0.05921713,0.00004883505,0.00009491991,0.001284615,0.0001873646,0.1360575,0.7965649,0.0007156064,0.002813306,0.00005088222],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.933943,0.0006547791,0.06277948,0.00007956564,0.0000540602,0.00004191635,0.0001566998,0.0009648312,0.001325713],"genre_scores_gemma":[0.9948554,0.00006501972,0.004591199,0.00001755548,0.00001105212,0.00001107656,0.00004328835,0.00002399753,0.0003814494],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001975186,"threshold_uncertainty_score":0.006607652,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008968384731269933,"score_gpt":0.2961093501449572,"score_spread":0.2871409654136873,"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."}}