{"id":"W4386919806","doi":"10.1109/sas58821.2023.10254176","title":"Critical Electrical Infrastructure Segmentation in Arctic Conditions","year":2023,"lang":"en","type":"article","venue":"","topic":"Structural Integrity and Reliability Analysis","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University","funders":"","keywords":"Critical infrastructure; Computer science; Arctic; Segmentation; Artificial intelligence; Geology; Computer security; Oceanography","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":[0.0001759052,0.000762525,0.0003135127,0.001964365,0.0005232056,0.0009349118,0.0005038953,0.0004699324,0.001813352],"category_scores_gemma":[0.0006306265,0.0002176496,0.0003213069,0.0008926607,0.0003570016,0.0005115762,0.0006141874,0.0003551732,0.0007806657],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00131001,"about_ca_system_score_gemma":0.001454408,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.1450592,"about_ca_topic_score_gemma":0.2956473,"domain_scores_codex":[0.9997625,0.0000152886,0.000007488084,0.00007269257,0.00004414278,0.00009789638],"domain_scores_gemma":[0.9997912,0.00002546868,0.00002920572,0.00002185856,0.0001025214,0.00002972772],"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":[0.0009970458,0.0001992764,0.1313,0.0003852722,0.0001199613,0.002037227,0.001466368,0.3151318,0.08588865,0.003804987,0.01974198,0.4389275],"study_design_scores_gemma":[0.00001856499,0.00009363073,0.1393659,0.0001009335,0.00007156742,0.0006408276,0.002209379,0.8040606,0.0351789,0.003404548,0.0148027,0.00005255971],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8601632,0.0008818225,0.108636,0.0004218259,0.0001269322,0.0001461404,0.004112817,0.002916911,0.02259439],"genre_scores_gemma":[0.9718621,0.0002274608,0.02058999,0.00006052086,0.00001934581,0.00001835475,0.003734577,0.0001385046,0.003349192],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1450592,"threshold_uncertainty_score":0.2884298,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00851139535267591,"score_gpt":0.2737491508784599,"score_spread":0.265237755525784,"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."}}