{"id":"W2764181884","doi":"10.1007/978-3-319-68454-3_3","title":"How Can Material Characterization Support Cable Aging Management?","year":2017,"lang":"en","type":"book-chapter","venue":"˜The œminerals, metals & materials series","topic":"High voltage insulation and dielectric phenomena","field":"Materials Science","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"Kinectrics (Canada)","funders":"","keywords":"Life extension; Reliability engineering; Characterization (materials science); Engineering; Forensic engineering; Power consumption; Instrumentation (computer programming); Computer science; Power (physics); Materials science","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":[0.001249929,0.0007897873,0.0006245834,0.001087338,0.0003106357,0.00240528,0.0009095459,0.001175829,0.007417148],"category_scores_gemma":[0.003233921,0.000284436,0.0002923024,0.0005585465,0.000500483,0.004747901,0.0006321059,0.0008430998,0.006356061],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003776862,"about_ca_system_score_gemma":0.0009241016,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001696274,"about_ca_topic_score_gemma":0.002856459,"domain_scores_codex":[0.999709,0.00003831638,0.00001415304,0.00005369673,0.0001560857,0.00002866421],"domain_scores_gemma":[0.9986393,0.000406871,0.0001127717,0.0001316773,0.000648281,0.00006103306],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0001160157,0.0001097507,0.00286281,0.0005051457,0.00004087435,0.0001551333,0.0001420403,0.004196741,0.03464977,0.0150796,0.07573543,0.8664067],"study_design_scores_gemma":[0.00003465756,0.0002683573,0.009091777,0.001281098,0.0001878154,0.001206691,0.001056152,0.04347679,0.08573297,0.1067188,0.750792,0.0001529045],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.07456598,0.09508518,0.5063334,0.05392187,0.01142641,0.0002434522,0.003105146,0.01304298,0.2422757],"genre_scores_gemma":[0.4299713,0.09210213,0.2848788,0.008899724,0.004159245,0.0001845385,0.003125953,0.002375367,0.1743028],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.007417148,"threshold_uncertainty_score":0.02481282,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01903927738280024,"score_gpt":0.2249354767226134,"score_spread":0.2058961993398131,"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."}}