{"id":"W4393904469","doi":"10.48550/arxiv.2404.00408","title":"Deep Learning with Parametric Lenses","year":2024,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Artificial intelligence; Parametric statistics; Computer science; Optometry; Mathematics; Medicine; Statistics","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":[0.001638151,0.0007221069,0.0005533643,0.001833842,0.0005598585,0.003780079,0.001631001,0.001018809,0.005501426],"category_scores_gemma":[0.007092321,0.0004053017,0.0009908793,0.001433288,0.003133668,0.006766017,0.004513817,0.00307976,0.001415655],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001674401,"about_ca_system_score_gemma":0.0009214033,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00171331,"about_ca_topic_score_gemma":0.001873455,"domain_scores_codex":[0.9985144,0.0003900762,0.00009683194,0.0002900367,0.0005814023,0.0001274284],"domain_scores_gemma":[0.9983235,0.0006382223,0.0001569939,0.0004363525,0.0003060876,0.0001388411],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00003276606,0.00001574699,0.0004101439,0.00006893565,0.00001805456,0.00005325654,0.0001175539,0.01853437,0.001395429,0.9196836,0.00181786,0.05785223],"study_design_scores_gemma":[0.000007457526,0.00003562904,0.0001657177,0.00003732727,0.000009473358,0.00009058903,0.0000444505,0.1599224,0.002158575,0.8247102,0.01280075,0.00001738697],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.005368992,0.0003052564,0.9876478,0.0004532883,0.00005147789,0.00001799283,0.0001164046,0.0005706499,0.005468141],"genre_scores_gemma":[0.5426669,0.001271555,0.4404028,0.0007730394,0.0002683969,0.0002304426,0.0005377163,0.0006482448,0.01320103],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005501426,"threshold_uncertainty_score":0.01840407,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05353749610784696,"score_gpt":0.1771303171505225,"score_spread":0.1235928210426756,"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."}}