{"id":"W3204384816","doi":"10.18280/ts.380420","title":"Thermal Fault Diagnosis of Electrical Equipment in Substations Based on Image Fusion","year":2021,"lang":"en","type":"article","venue":"Traitement du signal","topic":"Fire Detection and Safety Systems","field":"Engineering","cited_by":20,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Fault (geology); Electrical equipment; Artificial intelligence; Convolutional neural network; Computer science; Image (mathematics); Infrared; Fusion; Thermal; Artificial neural network; Segmentation; Computer vision; Image fusion; Pattern recognition (psychology); Engineering; Electrical engineering","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0001456012,0.00009988473,0.0001428252,0.0001165511,0.00002711692,0.00001329398,0.0000529081,0.00004287234,0.0009278374],"category_scores_gemma":[0.00001418978,0.00009979975,0.00006594072,0.0002929873,0.00001011359,0.00004312708,0.000006070016,0.00009996626,0.00002160824],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009661201,"about_ca_system_score_gemma":0.00002263638,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001564381,"about_ca_topic_score_gemma":0.00003651947,"domain_scores_codex":[0.9990819,0.00006570255,0.0003183939,0.0001231361,0.0002474733,0.0001634453],"domain_scores_gemma":[0.9996818,0.0001147108,0.00002652177,0.00009521934,0.00003778422,0.0000439453],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001173114,0.0008673015,0.007036487,0.0001906455,0.00006019022,0.0001067907,0.001264273,0.4841636,0.4740078,0.0003369958,0.001863654,0.02998494],"study_design_scores_gemma":[0.001538996,0.0001825325,0.05057504,0.0001055268,0.00001298468,0.000001572222,0.0001236534,0.7398183,0.2057396,0.000009408043,0.001694248,0.0001981479],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9885946,0.00007979378,0.007901214,0.0002345884,0.0001498486,0.0002153407,0.00002102776,0.00008896321,0.002714621],"genre_scores_gemma":[0.9994527,0.00001563017,0.0002625113,0.00008714638,0.00003550643,0.00008431799,0.00002592,0.00001442957,0.00002185211],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2682682,"threshold_uncertainty_score":0.9999855,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01103750040826883,"score_gpt":0.2181308174452485,"score_spread":0.2070933170369796,"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."}}