{"id":"W2582306747","doi":"10.1002/jbio.201600264","title":"Differential standard deviation of log‐scale intensity based optical coherence tomography angiography","year":2017,"lang":"en","type":"article","venue":"Journal of Biophotonics","topic":"Optical Coherence Tomography Applications","field":"Engineering","cited_by":17,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto; Sunnybrook Health Science Centre; Health Sciences Centre; Toronto Metropolitan University","funders":"National Natural Science Foundation of China","keywords":"Standard deviation; Imaging phantom; Optical coherence tomography; Image resolution; Optics; Pixel; Speckle pattern; Intensity (physics); Mathematics; Tomography; Physics; Statistics","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006432364,0.0002944974,0.0002788837,0.001225726,0.000136656,0.0005962401,0.0005599548,0.0003024836,0.0008821591],"category_scores_gemma":[0.002627177,0.0001928164,0.000277492,0.0005909934,0.0002215784,0.0005682549,0.0004418942,0.0003421408,0.0002699539],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003654569,"about_ca_system_score_gemma":0.0003677841,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006003695,"about_ca_topic_score_gemma":0.001016179,"domain_scores_codex":[0.9993829,0.0001019159,0.00003797065,0.00009006843,0.0003530693,0.00003398218],"domain_scores_gemma":[0.9990348,0.0003529816,0.0001214243,0.0001112634,0.0003402802,0.00003933503],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0004525912,0.0001068928,0.01523462,0.0003371929,0.00009296306,0.0002903704,0.000137637,0.01697708,0.433143,0.005714342,0.002026779,0.5254864],"study_design_scores_gemma":[0.00006946558,0.0003859016,0.04519915,0.00004469833,0.0001340416,0.003698746,0.00008087549,0.5202266,0.4163862,0.004068709,0.009510329,0.0001953202],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1364279,0.000759171,0.858691,0.0001581744,0.00005946934,0.00006325059,0.0001774137,0.001344285,0.002319296],"genre_scores_gemma":[0.6552585,0.0005810452,0.3420353,0.000139815,0.00005103532,0.0001268915,0.0003551414,0.0002196194,0.001232698],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001225726,"threshold_uncertainty_score":0.003401816,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01138627246430302,"score_gpt":0.2384108202681332,"score_spread":0.2270245478038301,"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."}}