{"id":"W4391096024","doi":"10.1109/icecet58911.2023.10389205","title":"Dental Imaging Using a Higher Signal-to-Noise Ratio Optical Coherence Tomography System","year":2023,"lang":"en","type":"article","venue":"","topic":"Optical Coherence Tomography Applications","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Manitoba","funders":"","keywords":"Optical coherence tomography; Signal-to-noise ratio (imaging); Image quality; Fast Fourier transform; Computer science; Coherence (philosophical gambling strategy); Iterative reconstruction; Noise (video); Computer vision; Fourier transform; SIGNAL (programming language); Artificial intelligence; Optics; Peak signal-to-noise ratio; Frequency domain; Image (mathematics); Algorithm; Mathematics; Physics; Telecommunications","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":[0.0004751502,0.0002262848,0.0002924014,0.0004324505,0.0001824355,0.0004735522,0.0002821266,0.000473932,0.0006605211],"category_scores_gemma":[0.0007502668,0.0001750221,0.0001732152,0.0003326455,0.000302419,0.000529002,0.0003242148,0.0002815108,0.0001953066],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002794437,"about_ca_system_score_gemma":0.0004094783,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006796006,"about_ca_topic_score_gemma":0.001391578,"domain_scores_codex":[0.9997024,0.00007292609,0.00001836817,0.00005930138,0.0001293601,0.00001767415],"domain_scores_gemma":[0.9996542,0.0001525977,0.00005484395,0.00004393005,0.00007211343,0.00002238055],"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":[0.0001044212,0.00003688597,0.0006557722,0.00009077397,0.000008328874,0.0001329034,0.00004107694,0.0007077521,0.9614857,0.0006560098,0.0001207606,0.03595961],"study_design_scores_gemma":[0.00008242298,0.001341995,0.01234127,0.00005491318,0.000117674,0.004662804,0.0001123625,0.05842087,0.9074574,0.0004995873,0.0148148,0.0000938013],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.4986909,0.001847998,0.494063,0.0005481104,0.0001026316,0.0001634584,0.0001409694,0.0005877555,0.003855212],"genre_scores_gemma":[0.4474457,0.0008859422,0.5500931,0.0001883672,0.00005477143,0.00007161303,0.00006035991,0.00002625782,0.001174044],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0006796006,"threshold_uncertainty_score":0.002512872,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01634862011286798,"score_gpt":0.2450691681672002,"score_spread":0.2287205480543322,"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."}}