{"id":"W4393423638","doi":"10.5281/zenodo.7613425","title":"Electric Power Fuse Identification with Deep Learning","year":2023,"lang":"en","type":"dataset","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Advanced Battery Technologies Research","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University; Université de Sherbrooke","funders":"","keywords":"Fuse (electrical); Identification (biology); Power (physics); Computer science; Artificial intelligence; Electrical engineering; Engineering; Physics; Biology","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":[0.0003482684,0.001496544,0.0005913608,0.001809814,0.0003454293,0.001108555,0.001228314,0.001245169,0.009983541],"category_scores_gemma":[0.00118499,0.0006536268,0.001092577,0.001034374,0.0002327494,0.001228581,0.001400598,0.001309091,0.009569039],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006786567,"about_ca_system_score_gemma":0.0006607031,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006647117,"about_ca_topic_score_gemma":0.00982062,"domain_scores_codex":[0.9997314,0.00002316819,0.00001316138,0.00009570501,0.00008599732,0.00005060992],"domain_scores_gemma":[0.9997186,0.00005806555,0.00002604988,0.00006185103,0.0001155483,0.00001972764],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0003122476,0.0002166814,0.003577005,0.0003153261,0.0001545805,0.0002487079,0.00007044243,0.2103223,0.01564649,0.002016111,0.06536783,0.7017524],"study_design_scores_gemma":[0.0000145203,0.000048891,0.001196187,0.00005961715,0.00002742052,0.0001024994,0.00005019999,0.9697015,0.01273079,0.004375285,0.01166697,0.00002614489],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"dataset","genre_scores_codex":[0.04259749,0.001724216,0.8785285,0.0007911869,0.0004764331,0.000144371,0.007487655,0.05293667,0.01531336],"genre_scores_gemma":[0.5249879,0.001625991,0.3700713,0.0009351501,0.000233341,0.0003199458,0.04526669,0.002004532,0.05455515],"genre_candidate":"dataset","genre_consensus":null,"teacher_disagreement_score":0.009983541,"threshold_uncertainty_score":0.03339827,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01705838157458494,"score_gpt":0.2356334723469941,"score_spread":0.2185750907724092,"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."}}