{"id":"W2045490091","doi":"10.1364/ol.39.003472","title":"Speckle statistics in OCT images: Monte Carlo simulations and experimental studies","year":2014,"lang":"en","type":"article","venue":"Optics Letters","topic":"Optical Coherence Tomography Applications","field":"Engineering","cited_by":61,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University; 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; Russian Foundation for Basic Research","keywords":"Speckle pattern; Monte Carlo method; Optical coherence tomography; Optics; Coherence (philosophical gambling strategy); Speckle noise; Speckle imaging; Image processing; Computer science; Physics; Statistics; Artificial intelligence; Image (mathematics); 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":[0.001922737,0.0003951012,0.0005685778,0.000822605,0.0004377269,0.0007564455,0.0005303217,0.001143721,0.0006424795],"category_scores_gemma":[0.008243883,0.0004030328,0.0003883303,0.0009026346,0.0008551189,0.0009313381,0.0002703108,0.00053066,0.00008779277],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001038959,"about_ca_system_score_gemma":0.0006572965,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008160532,"about_ca_topic_score_gemma":0.004460994,"domain_scores_codex":[0.9995885,0.0001598737,0.00002476992,0.00004025797,0.0001467775,0.00003976012],"domain_scores_gemma":[0.9931198,0.005601131,0.0004052598,0.0002473227,0.0005341193,0.00009229654],"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":[0.00005825519,0.00006428093,0.001655856,0.00004585319,0.00001489287,0.00008933157,0.00007006295,0.986997,0.001883924,0.006160403,0.000216204,0.002743833],"study_design_scores_gemma":[0.000004556848,0.000009012085,0.0002210221,0.00000538683,0.000002257137,0.00001464323,0.000005337437,0.9983765,0.0006198831,0.0006641155,0.00007173356,0.000005583305],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8159927,0.001318426,0.1711069,0.0006054174,0.00004879317,0.0001648611,0.0004355994,0.0003459731,0.009981293],"genre_scores_gemma":[0.9741197,0.0004546503,0.02447953,0.00006995467,0.00002016399,0.00009765601,0.0001284683,0.00005616959,0.0005737891],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.008160532,"threshold_uncertainty_score":0.01622605,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01323477808809775,"score_gpt":0.2636470960625151,"score_spread":0.2504123179744173,"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."}}