{"id":"W2413060559","doi":"10.1364/boe.7.002551","title":"Analysis of scattering statistics and governing distribution functions in optical coherence tomography","year":2016,"lang":"en","type":"article","venue":"Biomedical Optics Express","topic":"Optical Coherence Tomography Applications","field":"Engineering","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"University Health Network; University of Waterloo; University of Toronto","funders":"Canadian Institutes of Health Research; Natural Sciences and Engineering Research Council of Canada; Ministry of Education and Science of the Russian Federation; California HIV/AIDS Research Program","keywords":"Optical coherence tomography; Optics; Coherence (philosophical gambling strategy); Light scattering; Diffuse optical imaging; Tomography; Computer science; Physics; Scattering; Medical physics; Statistics; Statistical physics; Mathematics","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002455456,0.0005185261,0.0003612204,0.001707236,0.0003478467,0.001041281,0.0005581939,0.0005960405,0.0003955037],"category_scores_gemma":[0.009815015,0.0003753459,0.0003537953,0.001459463,0.001177404,0.001539359,0.0004718861,0.0006639539,0.0002009929],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001060467,"about_ca_system_score_gemma":0.0008224138,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003412241,"about_ca_topic_score_gemma":0.001465261,"domain_scores_codex":[0.9992592,0.0002566137,0.00003965517,0.0001003376,0.0002830681,0.00006119029],"domain_scores_gemma":[0.9952101,0.003620666,0.0005046368,0.0002662492,0.0003346249,0.00006363514],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0001224406,0.000114252,0.009932038,0.0002110969,0.0000653379,0.000494439,0.0003055902,0.6512752,0.05447626,0.2163979,0.001335936,0.06526945],"study_design_scores_gemma":[0.000004085934,0.00002399116,0.00389021,0.00002066773,0.000006155853,0.0001855728,0.00003474296,0.9555352,0.005514242,0.0336328,0.001118617,0.00003367847],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0727637,0.0007071178,0.9242688,0.0001809723,0.00001168312,0.00003452574,0.0002106829,0.0003349349,0.001487651],"genre_scores_gemma":[0.8506489,0.002313628,0.1440625,0.0001224941,0.00007438428,0.0002019494,0.0006964747,0.0002432291,0.001636407],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003412241,"threshold_uncertainty_score":0.01298589,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00885464146645114,"score_gpt":0.2296805486151048,"score_spread":0.2208259071486537,"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."}}