{"id":"W4321325446","doi":"10.2316/j.2023.203-0435","title":"AN INTELLIGENT FUSION OBJECT-DETECTION ALGORITHM FOR SMART SUBSTATION SYSTEM, 1-7.","year":2023,"lang":"en","type":"article","venue":"International Journal of Power and Energy Systems","topic":"Advanced Algorithms and Applications","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Fusion; Object (grammar); Computer science; Sensor fusion; Artificial intelligence; Algorithm; Computer vision","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009972968,0.00118805,0.001032443,0.001768884,0.0006014076,0.0008326894,0.00112398,0.001287545,0.002876732],"category_scores_gemma":[0.001457215,0.0003793547,0.001049952,0.001184747,0.0003351155,0.001170921,0.001110159,0.0008168353,0.002069055],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007248065,"about_ca_system_score_gemma":0.0007889016,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005063,"about_ca_topic_score_gemma":0.005762879,"domain_scores_codex":[0.9994587,0.00006056503,0.00003912159,0.0001443951,0.0002329031,0.00006427108],"domain_scores_gemma":[0.9996364,0.00006423685,0.00004956418,0.00006345978,0.0001662845,0.00002008007],"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":[0.0003362335,0.0001052864,0.003194064,0.00009935423,0.0001417343,0.00009556133,0.00006052759,0.04733804,0.0219732,0.001871468,0.009993018,0.9147915],"study_design_scores_gemma":[0.00002968462,0.0001043236,0.003377345,0.00001322457,0.00004616206,0.0001851476,0.00002996355,0.9643747,0.02108618,0.003121876,0.007604381,0.00002695564],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0214107,0.0006593883,0.968347,0.0001195832,0.0001249075,0.0001213028,0.0003357192,0.007428125,0.001453326],"genre_scores_gemma":[0.16655,0.0002629482,0.8262063,0.000143156,0.00005504296,0.0001500109,0.002060114,0.0002744961,0.004297936],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005063,"threshold_uncertainty_score":0.01006705,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008030014811221783,"score_gpt":0.249986016231793,"score_spread":0.2419560014205712,"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."}}