{"id":"W4396217134","doi":"10.3390/electronics13091721","title":"Explainable Artificial Intelligence Approach for Diagnosing Faults in an Induction Furnace","year":2024,"lang":"en","type":"article","venue":"Electronics","topic":"Non-Destructive Testing Techniques","field":"Engineering","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of New Brunswick; University of Windsor","funders":"","keywords":"Interpretability; Artificial neural network; Outlier; Deep learning; Artificial intelligence; Computer science; Machine learning; Harmonics; Fault (geology); Measure (data warehouse); Reliability engineering; Voltage; Data mining; Pattern recognition (psychology); Engineering; Electrical engineering","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":[0.0003112026,0.0001352206,0.0001131345,0.0001691456,0.00004781165,0.00007265905,0.0001313702,0.00009738189,0.000002917157],"category_scores_gemma":[0.00007315807,0.0001531834,0.00002985573,0.000394431,0.00002055378,0.0003503717,0.00001456438,0.0003627013,0.000002588021],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004760586,"about_ca_system_score_gemma":0.00005010147,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000006594091,"about_ca_topic_score_gemma":0.00004198045,"domain_scores_codex":[0.9990774,0.00002026832,0.0001888072,0.0002433851,0.00008363574,0.0003865493],"domain_scores_gemma":[0.9996791,0.00009361389,0.00001354973,0.0001512426,0.00002890041,0.00003356131],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.00005050405,0.000237154,0.0003395043,0.0009020809,0.00004926624,0.00002282438,0.002516367,0.03088795,0.1189516,0.5648178,0.0003901892,0.2808348],"study_design_scores_gemma":[0.00003212291,0.0002899583,0.00004785901,0.0001098523,0.00001184385,0.00002751377,0.0001975396,0.3198223,0.1281925,0.5503216,0.0006202026,0.0003266168],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3678941,0.002980663,0.6262938,0.00003432711,0.0002133612,0.0004041707,0.000002845149,0.001412364,0.0007643608],"genre_scores_gemma":[0.7745131,0.0001165649,0.2249639,0.000005333483,0.0001495135,0.0001776373,0.0000176704,0.00005241213,0.000003862423],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.406619,"threshold_uncertainty_score":0.6246636,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03748760693804111,"score_gpt":0.2941908501454573,"score_spread":0.2567032432074162,"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."}}