{"id":"W2945053806","doi":"10.21037/qims.2019.05.17","title":"Multi-scale and -contrast sensorless adaptive optics optical coherence tomography","year":2019,"lang":"en","type":"article","venue":"Quantitative Imaging in Medicine and Surgery","topic":"Optical Coherence Tomography Applications","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Retinal; Optical coherence tomography; Adaptive optics; Retina; Retinal pigment epithelium; Contrast (vision); Optics; Computer science; Preclinical imaging; Visualization; Image quality; Biomedical engineering; Computer vision; Artificial intelligence; Ophthalmology; In vivo; Medicine; Physics; Biology","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":[0.0004243335,0.000217146,0.0004502755,0.0003799162,0.00004270298,0.00002876931,0.000071228,0.00005483757,0.00002706777],"category_scores_gemma":[0.0001011754,0.0001963074,0.00004524542,0.0005324032,0.0005631828,0.0001982441,0.00002451863,0.0002892663,0.00001270164],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001428389,"about_ca_system_score_gemma":0.00001418865,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005312404,"about_ca_topic_score_gemma":0.00003804645,"domain_scores_codex":[0.9987712,0.00004958123,0.0003500892,0.0003206663,0.0001709509,0.0003375069],"domain_scores_gemma":[0.9979278,0.001623325,0.00003761239,0.0001742816,0.00008576147,0.0001511784],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00005230961,0.00009829867,0.9465131,0.0002365107,0.00007171504,0.00004898129,0.002277776,0.0003556183,0.02826747,0.01063829,0.0005950427,0.01084497],"study_design_scores_gemma":[0.001308796,0.0001225648,0.5721493,0.0008846349,0.00006951577,0.00003858446,0.009478206,0.4127576,0.0007284636,0.001389165,0.0003167356,0.0007563395],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9742188,0.002909273,0.0188255,0.000599653,0.0001581848,0.0003697145,0.00001107262,0.0001397382,0.002768022],"genre_scores_gemma":[0.975376,0.0003573862,0.02402582,0.000131811,0.00002450516,0.00003990629,0.00000660845,0.00002540894,0.00001261251],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.412402,"threshold_uncertainty_score":0.8005185,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03340744002882708,"score_gpt":0.2837487741202061,"score_spread":0.250341334091379,"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."}}