{"id":"W2982819275","doi":"10.1038/s41598-019-53170-w","title":"3D Dental Subsurface Imaging Using Enhanced Truncated Correlation-Photothermal Coherence Tomography","year":2019,"lang":"en","type":"article","venue":"Scientific Reports","topic":"Photoacoustic and Ultrasonic Imaging","field":"Engineering","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"Xanadu Quantum Technologies (Canada); University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Institutes of Health Research; University of Toronto; Canada Research Chairs; Government of Canada; California HIV/AIDS Research Program","keywords":"Optical coherence tomography; Tomography; Tomographic reconstruction; Medical imaging; Ionizing radiation; Medicine; Medical physics; Computer science; Radiology; Physics","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":[0.0004904339,0.0002092203,0.0002042739,0.0001866246,0.0001902483,0.0002600611,0.0001402648,0.00005001212,0.0007177927],"category_scores_gemma":[0.00002703279,0.0002186658,0.0000928409,0.000675164,0.0001318553,0.0004340536,0.0000312132,0.0001821818,0.00008391749],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001092081,"about_ca_system_score_gemma":0.00008115407,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004999899,"about_ca_topic_score_gemma":0.000005119754,"domain_scores_codex":[0.9981207,0.00001863252,0.0004278862,0.0005375146,0.0004218965,0.0004733625],"domain_scores_gemma":[0.9990726,0.00003721366,0.0001334029,0.0005562652,0.00009334405,0.000107211],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000002986864,0.00001506426,0.04274605,0.00003147468,0.00001934535,0.00006874956,0.0003291574,0.0907316,0.8651623,0.000002781213,0.0003687149,0.0005218444],"study_design_scores_gemma":[0.000253957,0.000005166487,0.004120969,0.0001327717,0.00003607653,0.0004203835,0.0003660306,0.7407156,0.2526092,0.0001770392,0.0007024801,0.0004603353],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9534554,0.0002630657,0.02851721,0.000001950215,0.01043673,0.0003576595,0.000004224928,0.0003672751,0.006596415],"genre_scores_gemma":[0.9974982,0.000001303107,0.001783546,0.0000132082,0.0000290022,0.000007854267,0.00003268802,0.0000406784,0.0005935803],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.649984,"threshold_uncertainty_score":0.8916931,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.005026921933119708,"score_gpt":0.2036544422717455,"score_spread":0.1986275203386258,"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."}}