{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00004852348,0.0001105394,0.0001274022,0.00007965256,0.00004339791,0.00003466958,0.00006477682,0.00002525736,0.000008217877],"category_scores_gemma":[0.00003124967,0.0001204401,0.0000157211,0.0001353036,0.0001098154,0.00007881817,0.00002600495,0.0001061114,0.000009704473],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003992975,"about_ca_system_score_gemma":0.000002024729,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00000836889,"about_ca_topic_score_gemma":0.00002027118,"domain_scores_codex":[0.9994455,0.0000128898,0.0001565034,0.0001289317,0.00008615555,0.0001700511],"domain_scores_gemma":[0.9995859,0.0001717449,0.00001394326,0.0001589697,0.00001894494,0.00005050184],"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":[0.000007552237,0.0001109013,0.009010175,0.0001333621,0.0001511356,0.00001458023,0.002773772,0.8497313,0.1169366,0.0137509,0.005761548,0.001618101],"study_design_scores_gemma":[0.0009734628,0.00008229166,0.01236893,0.00005708705,0.00005560531,0.00000391191,0.001082391,0.9732656,0.009004844,0.001014279,0.001401815,0.0006897515],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9801586,0.000312529,0.0180173,0.0003330676,0.00008312467,0.0001838191,0.00005265649,0.0001053286,0.0007536286],"genre_scores_gemma":[0.9589631,0.0000285105,0.04078388,0.0001281461,0.00003524124,0.00002663539,0.000004444414,0.00001864153,0.00001139112],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1235343,"threshold_uncertainty_score":0.4911403,"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."}}