{"id":"W955536151","doi":"","title":"Rapid Volumetric OCT Image Acquisition Using Compressive Sampling","year":2011,"lang":"en","type":"article","venue":"Investigative Ophthalmology & Visual Science","topic":"Optical Coherence Tomography Applications","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Compressed sensing; Sampling (signal processing); Biomedical engineering; Computer vision; Computer science; Artificial intelligence; 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":["metaepi_narrow","sts"],"consensus_categories":[],"category_scores_codex":[0.0004111183,0.0002403974,0.0002372193,0.0005207831,0.0004107607,0.00006781729,0.0006029368,0.0001126167,0.0004446097],"category_scores_gemma":[0.0001851198,0.0002481485,0.0000659588,0.003294358,0.004231847,0.0007680164,0.0001510991,0.0002999252,0.0001294978],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001373378,"about_ca_system_score_gemma":0.0001007778,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00007286555,"about_ca_topic_score_gemma":4.318624e-7,"domain_scores_codex":[0.9981771,0.00007086337,0.0003114283,0.0005104095,0.0003169358,0.0006132231],"domain_scores_gemma":[0.998857,0.0001400738,0.0000945728,0.0003047053,0.0002626852,0.0003410265],"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":[0.000004559799,0.00006097624,0.006296251,0.00001427929,0.0000184477,0.00002626037,0.0006265206,0.0003640726,0.9914646,0.0005722293,0.000009923656,0.000541914],"study_design_scores_gemma":[0.0002587944,0.0003345002,0.2663707,0.00006231376,0.00004771964,0.000227699,0.0003557881,0.07096738,0.6420929,0.01861742,0.00001679468,0.0006479722],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9874011,0.0001410081,0.00198591,0.00001044951,0.0002098785,0.0003089979,0.00001313882,0.000226976,0.009702504],"genre_scores_gemma":[0.9339863,0.000003590795,0.06584561,0.00003537377,0.0000442804,0.00005259595,0.000004156268,0.00002264661,0.000005460668],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3493717,"threshold_uncertainty_score":0.9999971,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1000292474235533,"score_gpt":0.336977139948242,"score_spread":0.2369478925246887,"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."}}