{"id":"W4403726067","doi":"10.36227/techrxiv.172979029.94476708/v1","title":"Probing Armour Losses Formulae from CIGRE Technical Brochure 908 up to 15 kHz","year":2024,"lang":"en","type":"preprint","venue":"","topic":"Superconducting Materials and Applications","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Armour; Physics; Environmental science; Optics; Materials science; Engineering physics; Computer science; Engineering; Composite material","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":[0.001549218,0.0009673993,0.0006215492,0.00228755,0.000618162,0.001366093,0.001609681,0.001087231,0.004836407],"category_scores_gemma":[0.006592638,0.0004794599,0.00058523,0.0008926079,0.0007226308,0.001707064,0.001140101,0.001558373,0.002756005],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001258002,"about_ca_system_score_gemma":0.0005893464,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002831714,"about_ca_topic_score_gemma":0.003171784,"domain_scores_codex":[0.9972434,0.0003647136,0.0001024765,0.0003470859,0.001788185,0.0001540715],"domain_scores_gemma":[0.9982003,0.0007453908,0.0001884503,0.0003688098,0.0004754187,0.000021655],"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":[0.0006077578,0.0001455083,0.00519148,0.001927693,0.0002514824,0.001363247,0.002066512,0.1339457,0.2961427,0.2150518,0.02387521,0.3194309],"study_design_scores_gemma":[0.00004812144,0.0003294214,0.01008933,0.001042107,0.0001795652,0.00164552,0.0003966307,0.2695756,0.4682346,0.04411617,0.2040712,0.0002716963],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1240874,0.007168626,0.7738002,0.001201831,0.0008291768,0.0001130843,0.001012967,0.00558797,0.08619876],"genre_scores_gemma":[0.7670765,0.008118668,0.1834265,0.0006153174,0.0001994578,0.0002657477,0.001623759,0.003065259,0.03560881],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004836407,"threshold_uncertainty_score":0.01617938,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02588631064286207,"score_gpt":0.2599909023244051,"score_spread":0.234104591681543,"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."}}