{"id":"W2902545607","doi":"10.1364/boe.9.006529","title":"Buffer-averaging super-continuum source based spectral domain optical coherence tomography for high speed imaging","year":2018,"lang":"en","type":"article","venue":"Biomedical Optics Express","topic":"Optical Coherence Tomography Applications","field":"Engineering","cited_by":17,"is_retracted":false,"has_abstract":true,"ca_institutions":"Sunnybrook Health Science Centre; University of Toronto; Health Sciences Centre; Toronto Metropolitan University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Optical coherence tomography; Optics; Tomography; Diffuse optical imaging; Spectral imaging; Hyperspectral imaging; Optical tomography; Physics; Coherence (philosophical gambling strategy); Computer science; Artificial intelligence","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":[0.0003169602,0.00040807,0.0003624192,0.0002958888,0.0002479635,0.0004910933,0.001113671,0.0005055202,0.002731453],"category_scores_gemma":[0.0006195466,0.0002277878,0.0001108412,0.0004663973,0.0003483779,0.001437271,0.0007358957,0.0006160335,0.0006271319],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004758296,"about_ca_system_score_gemma":0.0004664344,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005076218,"about_ca_topic_score_gemma":0.001616321,"domain_scores_codex":[0.9998518,0.00002936588,0.000005502819,0.00003337147,0.0000638274,0.00001609593],"domain_scores_gemma":[0.9996629,0.0001416559,0.0000450701,0.00004944198,0.00006596022,0.00003495492],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0005703709,0.0001722161,0.0003910947,0.0001237786,0.00002203331,0.00007888109,0.00005974278,0.005686601,0.9460678,0.01016428,0.001945871,0.0347174],"study_design_scores_gemma":[0.00006040705,0.0002517901,0.0005360285,0.00001386568,0.00002562926,0.0001526829,0.00002927237,0.3245683,0.6659164,0.004150269,0.004260805,0.00003445335],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2043877,0.001562356,0.7831378,0.0005007477,0.0001901482,0.0001332617,0.0004705066,0.002119894,0.007497576],"genre_scores_gemma":[0.6710146,0.0005606672,0.3241904,0.0002198498,0.00005759614,0.0001412279,0.0004609003,0.0001557592,0.003199005],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002731453,"threshold_uncertainty_score":0.009137571,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008340722529278347,"score_gpt":0.2282454557728955,"score_spread":0.2199047332436171,"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."}}