{"id":"W917769808","doi":"","title":"How Accurate is Automated Segmentation of Optic Nerve Head Structures in Spectral Domain Optical Coherence Tomography","year":2012,"lang":"en","type":"article","venue":"Investigative Ophthalmology & Visual Science","topic":"Optical Coherence Tomography Applications","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"Dalhousie University","funders":"","keywords":"Optical coherence tomography; Segmentation; Optic nerve; Head (geology); Coherence (philosophical gambling strategy); Computer science; Optics; Domain (mathematical analysis); Computer vision; Artificial intelligence; Physics; Medicine; Anatomy; Geology; Mathematics","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":["metaepi_narrow","sts"],"consensus_categories":[],"category_scores_codex":[0.0005048272,0.0002937525,0.0003401144,0.0004788075,0.0001271758,0.00008259928,0.0005354639,0.0001546857,0.00008068318],"category_scores_gemma":[0.000132189,0.0002794473,0.00007699469,0.003297449,0.003980927,0.001028019,0.00009373627,0.0003419497,0.00001346782],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001216897,"about_ca_system_score_gemma":0.00009764803,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004335581,"about_ca_topic_score_gemma":0.00000396565,"domain_scores_codex":[0.9977707,0.0001017953,0.0004101063,0.0004272847,0.0004461716,0.0008439546],"domain_scores_gemma":[0.998886,0.0001807542,0.0001189801,0.0002826471,0.0001329653,0.0003985787],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.000009391058,0.0001049536,0.07817759,0.00003835657,0.00002479175,0.00002002338,0.001509935,0.000751425,0.9148399,0.004385396,0.00001912937,0.0001190742],"study_design_scores_gemma":[0.0002338264,0.0002069753,0.4567681,0.00003334888,0.00001404137,0.00009294346,0.0006254449,0.007019666,0.5257019,0.009016824,0.000001617015,0.0002853493],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9976195,0.0001400514,0.00009423311,0.0001631112,0.0001702576,0.0004968935,0.00002329484,0.0002221743,0.001070554],"genre_scores_gemma":[0.9734509,0.000001506456,0.0262999,0.00002180085,0.00003248024,0.0001616555,0.000007658324,0.00001892369,0.000005179457],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.389138,"threshold_uncertainty_score":0.9999658,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03535780284502501,"score_gpt":0.3319815059814958,"score_spread":0.2966237031364708,"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."}}