{"id":"W4310421030","doi":"10.48550/arxiv.2211.15088","title":"Class Adaptive Network Calibration","year":2022,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Machine Learning and Data Classification","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada; Compute Canada","keywords":"Smoothing; Computer science; Class (philosophy); Artificial intelligence; Machine learning; Code (set theory); Calibration; Artificial neural network; Scalability; Segmentation; Data mining; Mathematical optimization; Mathematics","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0003469356,0.0002179622,0.0002057705,0.0001375262,0.0003352846,0.0001474584,0.00174245,0.0001686287,0.000138375],"category_scores_gemma":[0.00002930361,0.0002669014,0.0001226335,0.0006402212,0.00004724013,0.0004153682,0.00263422,0.0008944641,0.00005379666],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002196237,"about_ca_system_score_gemma":0.0002091364,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001917136,"about_ca_topic_score_gemma":0.000040025,"domain_scores_codex":[0.9979949,0.0004167235,0.0001638587,0.001035525,0.000115688,0.000273303],"domain_scores_gemma":[0.9980648,0.0001124113,0.000302007,0.001350819,0.0000608662,0.0001091654],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00001302504,0.00002403955,0.001797996,0.000008900592,0.00002232948,0.00003814853,0.00006938969,0.5364226,0.000001972824,0.4585918,0.002281703,0.0007280642],"study_design_scores_gemma":[0.000153171,0.00004908158,0.00209521,0.00001585192,0.00002415003,0.000002147402,0.00004322483,0.9475896,0.000002244786,0.03286283,0.01688214,0.0002803212],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.008009299,0.00005906978,0.9793695,0.0004078537,0.0008623927,0.0002143006,0.00002200027,0.000436931,0.01061865],"genre_scores_gemma":[0.9917902,0.00007573666,0.00428408,0.0001904456,0.0001670424,0.000002639998,0.0002222704,0.0000149493,0.003252639],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9837809,"threshold_uncertainty_score":0.9999783,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07988316392125443,"score_gpt":0.1942131229630021,"score_spread":0.1143299590417477,"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."}}