{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006711198,0.0007548177,0.0004900002,0.0009493916,0.0003361023,0.0009692934,0.000465483,0.0005459464,0.003033131],"category_scores_gemma":[0.003164574,0.0005991831,0.0003037054,0.0009933742,0.0003950079,0.001359256,0.001514111,0.0009935503,0.0007335134],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003111309,"about_ca_system_score_gemma":0.001338386,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002106129,"about_ca_topic_score_gemma":0.004691144,"domain_scores_codex":[0.9993483,0.0001150989,0.00003629257,0.00005276048,0.0004044998,0.00004296372],"domain_scores_gemma":[0.9979621,0.0009287256,0.0002047787,0.0003279458,0.000481607,0.00009495703],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0007843057,0.000168286,0.002762323,0.0003445527,0.00009146096,0.0003019195,0.0002822259,0.0608567,0.3577166,0.009776918,0.005514384,0.5614005],"study_design_scores_gemma":[0.000064392,0.0002362991,0.003181057,0.00006004084,0.00003741562,0.001072848,0.0001033327,0.869893,0.1123751,0.006101017,0.006800136,0.00007543113],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03055362,0.0002406069,0.9653789,0.0002402031,0.00005598662,0.0001601128,0.0002800661,0.0007540613,0.002336277],"genre_scores_gemma":[0.1750454,0.0004517891,0.8220611,0.0001253484,0.00007000253,0.000184214,0.0005367598,0.000112905,0.001412601],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003033131,"threshold_uncertainty_score":0.01014686,"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."}}