{"id":"W2010181911","doi":"10.1364/oe.18.021003","title":"Rapid Volumetric OCT Image Acquisition Using Compressive Sampling","year":2010,"lang":"en","type":"article","venue":"Optics Express","topic":"Sparse and Compressive Sensing Techniques","field":"Engineering","cited_by":52,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia; Simon Fraser University","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Institutes of Health Research; Michael Smith Health Research BC","keywords":"Optical coherence tomography; Image quality; Compressed sensing; Computer vision; Computer science; Artificial intelligence; Iterative reconstruction; Sampling (signal processing); Data acquisition; Image processing; Optics; Image (mathematics); Physics","routes":{"ca_aff":true,"ca_fund":true,"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":[0.00009125246,0.0002193873,0.000222038,0.0002160478,0.0001298397,0.0001598121,0.0002615124,0.0001597023,0.00008815682],"category_scores_gemma":[0.00003496018,0.0002416418,0.00008063392,0.0002448779,0.00007147706,0.0002496576,0.00009507987,0.0004058224,0.00002325695],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003005202,"about_ca_system_score_gemma":0.0000107047,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001788294,"about_ca_topic_score_gemma":0.000001190428,"domain_scores_codex":[0.9989907,0.00001923778,0.0002309596,0.0002269065,0.0001959283,0.000336321],"domain_scores_gemma":[0.9991307,0.00008706682,0.00006062052,0.0004846099,0.0001391013,0.00009784802],"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":[0.000005277744,0.00002130484,0.00004290553,0.00001785893,0.00002629592,0.00001527501,0.00008262024,0.007250281,0.9880084,0.0002736888,0.0007750613,0.003481047],"study_design_scores_gemma":[0.0002468736,0.00002217925,0.0003119893,0.00008728436,0.000041461,0.00004171078,0.00003613369,0.4774977,0.5167361,0.0007197331,0.003841287,0.0004174778],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7390185,0.0003185558,0.2517311,0.000008397392,0.0009922439,0.0001904125,0.00001965713,0.001007058,0.00671407],"genre_scores_gemma":[0.7892617,0.00005586664,0.2102429,0.00002745206,0.000312892,0.000007226633,0.00001462652,0.00005893943,0.0000183669],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4712722,"threshold_uncertainty_score":0.9853867,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02773417782651211,"score_gpt":0.263534208529478,"score_spread":0.2358000307029659,"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."}}