{"id":"W4399798506","doi":"10.1049/icp.2024.0440","title":"Evaluating OLTC condition based on feature extraction from vibro-acoustic signals","year":2023,"lang":"en","type":"article","venue":"IET conference proceedings.","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Hydro-Québec; Université du Québec à Chicoutimi","funders":"","keywords":"Feature extraction; Computer science; Extraction (chemistry); Speech recognition; Feature (linguistics); Pattern recognition (psychology); Artificial intelligence","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":[0.000473286,0.0006076895,0.0004715147,0.002766742,0.0001294056,0.0005708209,0.0002332575,0.0005834231,0.0009388135],"category_scores_gemma":[0.001982387,0.0001043567,0.0002712143,0.0009994732,0.0002263299,0.0006522805,0.0002798113,0.0002430277,0.0004147951],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001466993,"about_ca_system_score_gemma":0.0001618645,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009240042,"about_ca_topic_score_gemma":0.0009252905,"domain_scores_codex":[0.9996672,0.00003604372,0.00003499022,0.00006507774,0.0001511594,0.00004560287],"domain_scores_gemma":[0.9991425,0.0003251683,0.0001711748,0.00005314255,0.0002678618,0.000040168],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.0008693145,0.0002753647,0.04209389,0.0004215061,0.00009897137,0.0007839426,0.0003175022,0.02909598,0.4373678,0.0006025639,0.001306826,0.4867662],"study_design_scores_gemma":[0.00003737391,0.001019246,0.2743421,0.00007501673,0.0001798383,0.001409374,0.0006781591,0.5465783,0.1721641,0.0009569911,0.002431918,0.0001274826],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6942603,0.000446373,0.3010814,0.00007171541,0.00005594664,0.0001144434,0.0004614967,0.001212485,0.002295739],"genre_scores_gemma":[0.9675667,0.0002142388,0.03123346,0.00001652767,0.00001921299,0.00003539891,0.0003657622,0.00003614922,0.0005125072],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002766742,"threshold_uncertainty_score":0.003140628,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06882331137763358,"score_gpt":0.3294396443484219,"score_spread":0.2606163329707883,"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."}}