{"id":"W4375858707","doi":"10.32473/flairs.36.133373","title":"Using Knowledge Graph Embedding for Fault Detection","year":2023,"lang":"en","type":"article","venue":"Proceedings of the ... International Florida Artificial Intelligence Research Society Conference","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Windsor","funders":"","keywords":"Automotive industry; Fault detection and isolation; Automotive engineering; Fault (geology); Electric vehicle; Computer science; Engineering; Business; Artificial intelligence; Power (physics); Actuator","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002164543,0.0001584634,0.0001875597,0.000264674,0.0004450458,0.0002499104,0.0006769999,0.0001840808,0.00002591335],"category_scores_gemma":[0.0005017546,0.000135776,0.0003105529,0.001478509,0.0001667455,0.0003232768,0.0001946564,0.000474994,0.00003809201],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002800384,"about_ca_system_score_gemma":0.00006904382,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005843582,"about_ca_topic_score_gemma":0.00001201839,"domain_scores_codex":[0.9980057,0.00001570254,0.0004997854,0.0002930491,0.0007454865,0.0004402918],"domain_scores_gemma":[0.99736,0.0002802955,0.00009633737,0.0001105205,0.00208105,0.00007183096],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00005775874,0.00002220172,0.00003985631,0.0001615633,0.0001031398,1.232834e-7,0.001994202,0.01554651,0.9253557,0.01831469,0.003400014,0.03500428],"study_design_scores_gemma":[0.00003167615,0.0000298826,0.00000983718,0.0001219436,0.000004722878,0.000001937492,0.002874973,0.509641,0.4723226,0.01263367,0.002232002,0.00009583322],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9062651,0.00006854981,0.08279327,0.0002371353,0.005656752,0.001071725,0.00004240559,0.0003654058,0.003499593],"genre_scores_gemma":[0.9981376,0.0001275274,0.0004807806,0.000004162642,0.0008834288,0.0001162779,0.000002349629,0.00003029789,0.0002175602],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4940945,"threshold_uncertainty_score":0.5536785,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2955318834200215,"score_gpt":0.4257532962685253,"score_spread":0.1302214128485037,"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."}}