{"id":"W2403984453","doi":"10.1371/journal.pone.0155319","title":"Prevalence and Distribution of Segmentation Errors in Macular Ganglion Cell Analysis of Healthy Eyes Using Cirrus HD-OCT","year":2016,"lang":"en","type":"article","venue":"PLoS ONE","topic":"Optical Coherence Tomography Applications","field":"Engineering","cited_by":30,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"","keywords":"Optical coherence tomography; Standard deviation; Artifact (error); Ganglion; Segmentation; Ophthalmology; Cirrus; Nuclear medicine; Medicine; Mathematics; Anatomy; Artificial intelligence; Computer science; Geology; Remote sensing","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004968802,0.0002167622,0.0002110353,0.001633468,0.0001759998,0.0002962112,0.0002031076,0.0003880881,0.0008846587],"category_scores_gemma":[0.002969834,0.000153611,0.0001399725,0.0005290585,0.0003858686,0.0004094579,0.0002773133,0.0001386178,0.0001438219],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001685399,"about_ca_system_score_gemma":0.0001473363,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001387039,"about_ca_topic_score_gemma":0.001757993,"domain_scores_codex":[0.9994013,0.00009344021,0.00009858041,0.0001301266,0.0002089441,0.00006760222],"domain_scores_gemma":[0.9977985,0.0005656244,0.0009771861,0.0001758058,0.0003470073,0.0001359109],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001967454,0.00001970867,0.9917447,0.00002084213,0.00002806151,0.000611249,0.0000913765,0.00004293777,0.002890318,0.00001094,0.00006236484,0.004280692],"study_design_scores_gemma":[0.000002859383,0.0001001811,0.9961786,0.000004183844,0.00001369094,0.002856024,0.00007765603,0.0001710357,0.0005164903,0.00001428047,0.00006206383,0.000002778676],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.999506,0.0002166834,0.00009393608,0.000007961473,0.000001486953,0.000003530996,0.00005523676,0.000005164883,0.0001101224],"genre_scores_gemma":[0.9996175,0.00006389076,0.0001690873,0.000007927886,0.000004253337,0.000002843251,0.00007991685,0.000002073055,0.00005239396],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001633468,"threshold_uncertainty_score":0.00295943,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02796253024419701,"score_gpt":0.2495842192348505,"score_spread":0.2216216889906535,"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."}}