{"id":"W2012407707","doi":"10.1364/ol.33.001530","title":"Speckle variance detection of microvasculature using swept-source optical coherence tomography","year":2008,"lang":"en","type":"article","venue":"Optics Letters","topic":"Optical Coherence Tomography Applications","field":"Engineering","cited_by":706,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Speckle pattern; Optical coherence tomography; Optics; Materials science; Speckle noise; Speckle imaging; Laser Doppler velocimetry; Coherence (philosophical gambling strategy); Microcirculation; Preclinical imaging; Biomedical engineering; Blood flow; Physics; In vivo; 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":[],"consensus_categories":[],"category_scores_codex":[0.00008045211,0.0002117229,0.000239252,0.0001733511,0.0001103458,0.0000231423,0.0002424153,0.0001394125,0.00002558859],"category_scores_gemma":[0.00001778111,0.000236947,0.0001851045,0.0008841414,0.0002952922,0.0001361057,0.00003209354,0.0003449107,0.00002085456],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004413462,"about_ca_system_score_gemma":0.00001231886,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001361201,"about_ca_topic_score_gemma":0.000003657621,"domain_scores_codex":[0.9988233,0.00001769972,0.0003064027,0.0002548644,0.0002545301,0.0003431699],"domain_scores_gemma":[0.9992334,0.00007177938,0.00005348969,0.000454978,0.00006952664,0.0001168909],"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":[0.000006124319,0.00004051845,0.001158928,0.00005109122,0.00008267635,0.000007673319,0.0001119612,0.04682047,0.9505613,0.0003236365,0.0001021865,0.0007334016],"study_design_scores_gemma":[0.001248516,0.0001480278,0.03712117,0.0001890226,0.0003293116,0.0003643683,0.0001151553,0.1762156,0.7785507,0.0002958108,0.003747592,0.001674687],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7430329,0.0001637264,0.2553812,0.00005892693,0.0001298384,0.0001984798,0.000006777337,0.0001897699,0.000838325],"genre_scores_gemma":[0.9232724,0.00002989598,0.07642607,0.0001065739,0.00009104711,0.00001881906,0.000004158235,0.00004107404,0.00001000728],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1802394,"threshold_uncertainty_score":0.9662418,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01129231525668456,"score_gpt":0.2016289979190344,"score_spread":0.1903366826623498,"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."}}