{"id":"W4234201315","doi":"10.32920/ryerson.14660907","title":"Prediction of Cup-to-Disc Ratio From Optic Fundus Image","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Retinal Imaging and Analysis","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Artificial intelligence; Optic cup (embryology); Optic disc; Feature (linguistics); Computer science; Pattern recognition (psychology); Fundus (uterus); Support vector machine; Pixel; Feature extraction; Image (mathematics); Computer vision; Mathematics; Glaucoma; Ophthalmology; Medicine","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":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0001462034,0.0001873805,0.0006033246,0.0001666684,0.00002510219,0.00006574719,0.000103949,0.0001415879,0.001505677],"category_scores_gemma":[0.0001894463,0.0001553026,0.0003051511,0.0001802219,0.00004383127,0.00004616215,0.0002331235,0.0004167618,0.00005464402],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005927164,"about_ca_system_score_gemma":0.0001770527,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001841088,"about_ca_topic_score_gemma":0.00001599287,"domain_scores_codex":[0.9985665,0.00005307755,0.0004319235,0.0004602607,0.0003493502,0.0001388698],"domain_scores_gemma":[0.9987071,0.00004442752,0.0001300963,0.0006618929,0.0003063308,0.0001501249],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.0003712566,0.001322542,0.08611596,0.00215741,0.004918277,0.0007663738,0.004112568,0.002264571,0.8434625,0.0001356482,0.04001455,0.01435831],"study_design_scores_gemma":[0.004591679,0.0008335583,0.3883762,0.0103369,0.01569855,0.0001419193,0.01053882,0.250208,0.3125208,0.001202695,0.00382821,0.001722665],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9070917,0.0005152395,0.06710926,0.003176041,0.0003787356,0.0002998372,0.0001088521,0.0001211932,0.02119915],"genre_scores_gemma":[0.9530683,0.00009933863,0.03517311,0.0002194171,0.0003832925,0.0000221471,0.001141638,0.00002564275,0.009867122],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5309417,"threshold_uncertainty_score":0.9994071,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03432263090817983,"score_gpt":0.2968946877394116,"score_spread":0.2625720568312317,"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."}}