{"id":"W2612758615","doi":"10.1186/s40942-017-0078-7","title":"Segmentation errors in macular ganglion cell analysis as determined by optical coherence tomography in eyes with macular pathology","year":2017,"lang":"en","type":"article","venue":"International Journal of Retina and Vitreous","topic":"Optical Coherence Tomography Applications","field":"Engineering","cited_by":21,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"","keywords":"Optical coherence tomography; Ganglion; Medicine; Ophthalmology; Outer plexiform layer; Inner plexiform layer; Segmentation; Retinal; Nerve fiber layer; Ganglion cell layer; Anatomy; Artificial intelligence; Computer science","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":[],"consensus_categories":[],"category_scores_codex":[0.0001800294,0.0001265898,0.0002251464,0.0005241113,0.00003646068,0.00009791015,0.0003201999,0.00007193624,0.00003380376],"category_scores_gemma":[0.00003113546,0.0001113131,0.00008880598,0.000227223,0.000119362,0.000204656,0.00002835577,0.0002129773,0.000001858722],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004305608,"about_ca_system_score_gemma":0.00001382127,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004570774,"about_ca_topic_score_gemma":0.0001164789,"domain_scores_codex":[0.9990645,0.00003002721,0.0003333404,0.0001467982,0.0002763538,0.0001489957],"domain_scores_gemma":[0.999423,0.00005720056,0.0001707154,0.0001440735,0.0001246114,0.0000803779],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0004461308,0.0003894073,0.8561024,0.00005506212,0.000651347,0.003470484,0.0007297714,0.006591537,0.1134906,0.0003130352,0.00009145313,0.01766876],"study_design_scores_gemma":[0.007773886,0.001476286,0.7800778,0.0004545354,0.0009889859,0.001434477,0.001152469,0.0408899,0.1597776,0.004133603,0.0005899387,0.001250474],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9954336,0.0003013307,0.002906415,0.0002203498,0.00006141341,0.0001004677,0.00001194137,0.00001149163,0.000952932],"genre_scores_gemma":[0.9957814,0.0002012251,0.003930373,0.00002207916,0.000021808,0.00001375468,0.000008463561,0.00001035008,0.0000105768],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.07602464,"threshold_uncertainty_score":0.4539215,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.005456151330351827,"score_gpt":0.2538509994186707,"score_spread":0.2483948480883189,"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."}}