{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004016713,0.001819332,0.001095263,0.001101876,0.0009397263,0.00180805,0.003731793,0.002461866,0.006102769],"category_scores_gemma":[0.01668595,0.0006395256,0.0008817263,0.001128087,0.001440997,0.003559517,0.003484651,0.003703314,0.002564929],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002075934,"about_ca_system_score_gemma":0.001719088,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003397221,"about_ca_topic_score_gemma":0.00430271,"domain_scores_codex":[0.9977963,0.0005020036,0.00009901048,0.0007994486,0.0005939808,0.0002093395],"domain_scores_gemma":[0.996106,0.001195988,0.0004459534,0.001135548,0.0009885741,0.0001279384],"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":[0.0003870337,0.0002301251,0.004791731,0.0003464385,0.0001625235,0.000116028,0.000372048,0.3851858,0.01784882,0.04299167,0.01778308,0.5297847],"study_design_scores_gemma":[0.00002752767,0.00004998794,0.0006127147,0.00005646819,0.00002669493,0.00007345872,0.00003355142,0.9615619,0.007857489,0.021683,0.007994013,0.00002321482],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02712543,0.001047229,0.959713,0.0006238596,0.0002706699,0.0001618971,0.000228927,0.00397341,0.006855564],"genre_scores_gemma":[0.5882004,0.000993485,0.3913059,0.001526991,0.0002670964,0.0008409562,0.001627591,0.002204521,0.01303316],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006102769,"threshold_uncertainty_score":0.02124268,"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."}}