{"id":"W4290875536","doi":"10.1145/3534678.3539219","title":"Towards Reliable Detection of Dielectric Hotspots in Thermal Images of the Underground Distribution Network","year":2022,"lang":"en","type":"article","venue":"Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining","topic":"Thermography and Photoacoustic Techniques","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Hydro-Québec","funders":"","keywords":"Hotspot (geology); Software deployment; Computer science; Truck; Segmentation; Visual inspection; Artificial intelligence; Image segmentation; Artificial neural network; Real-time computing; Data mining; Computer vision; Engineering; Geology; Automotive engineering","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.0003598375,0.0008780031,0.0004863395,0.001707464,0.000227086,0.0007705006,0.0007251491,0.0006870853,0.0006649161],"category_scores_gemma":[0.00109459,0.0002743754,0.0003428844,0.0007001564,0.0002901683,0.0005217099,0.000605027,0.0006627524,0.0006626813],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005340819,"about_ca_system_score_gemma":0.0006675508,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01215118,"about_ca_topic_score_gemma":0.02205432,"domain_scores_codex":[0.9997249,0.00003200368,0.000007654149,0.00008405706,0.00008665017,0.00006477067],"domain_scores_gemma":[0.9995781,0.00006424008,0.00005966032,0.0000536418,0.0002067124,0.00003757673],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0006623789,0.0002159768,0.01027031,0.0002641745,0.0001074655,0.00047127,0.0002343577,0.07083306,0.3370109,0.0009138396,0.009333629,0.5696827],"study_design_scores_gemma":[0.00001563096,0.00009126763,0.01914713,0.00002510228,0.00003666491,0.0002606057,0.0001298629,0.9064803,0.0698832,0.0008981204,0.003008816,0.00002326489],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3915442,0.0009934551,0.5945029,0.0003070685,0.0001173758,0.0001506782,0.001290526,0.006645139,0.004448605],"genre_scores_gemma":[0.8009419,0.000444372,0.1920394,0.0001274854,0.00007466296,0.00005118597,0.002193023,0.0002147794,0.003913179],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01215118,"threshold_uncertainty_score":0.02416092,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02300611810598622,"score_gpt":0.2420399321642931,"score_spread":0.2190338140583069,"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."}}