{"id":"W2065614805","doi":"10.1364/boe.5.004338","title":"Monte Carlo modeling of angiographic optical coherence tomography","year":2014,"lang":"en","type":"article","venue":"Biomedical Optics Express","topic":"Optical Coherence Tomography Applications","field":"Engineering","cited_by":19,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Institute of Biomedical Imaging and Bioengineering; National Cancer Institute; National Center for Research Resources; Natural Sciences and Engineering Research Council of Canada; National Institutes of Health","keywords":"Computer science; Optical coherence tomography; Monte Carlo method; Decorrelation; Algorithm; Coherence (philosophical gambling strategy); Angiography; Artificial intelligence; Image processing; Signal processing; Tomography; Medical imaging; Computer vision; Optics; Radiology; Image (mathematics); Physics; Medicine; Mathematics","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0002694172,0.0002668002,0.0003883736,0.0003956675,0.00005987969,0.00003810269,0.0005765192,0.00026746,0.00003589043],"category_scores_gemma":[0.00008253092,0.0002573666,0.0002710716,0.001199912,0.0005167149,0.0001121844,0.00008783797,0.0003155077,0.0000158436],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001308469,"about_ca_system_score_gemma":0.00001644408,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000168573,"about_ca_topic_score_gemma":0.000002075783,"domain_scores_codex":[0.9979898,0.00003293236,0.0005726462,0.0003318422,0.0005700683,0.000502754],"domain_scores_gemma":[0.998529,0.0002053539,0.00004797139,0.0006251786,0.0001430506,0.0004494732],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001383323,0.002420538,0.004909642,0.002092946,0.001451838,0.00003257354,0.001462009,0.4492715,0.3021005,0.177449,0.003110855,0.05556019],"study_design_scores_gemma":[0.0004185933,0.0001459332,0.0004871588,0.0001084743,0.00006458603,0.000003947151,0.00005839422,0.9926326,0.002449318,0.001773387,0.00145524,0.0004024296],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5267285,0.0005894025,0.4608368,0.0001269678,0.0003728745,0.0004640875,0.00006722868,0.0007430186,0.01007116],"genre_scores_gemma":[0.9594657,0.0000620512,0.0401724,0.00002225168,0.0001167624,0.000101491,0.000009349268,0.00004336969,0.000006613444],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.543361,"threshold_uncertainty_score":0.9999878,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01226466937780771,"score_gpt":0.2228544246800909,"score_spread":0.2105897553022832,"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."}}