{"id":"W1989970294","doi":"10.1038/nphoton.2014.111","title":"Truncated-correlation photothermal coherence tomography for deep subsurface analysis","year":2014,"lang":"en","type":"article","venue":"Nature Photonics","topic":"Photoacoustic and Ultrasonic Imaging","field":"Engineering","cited_by":95,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Photothermal therapy; Optical coherence tomography; Optics; Materials science; Planar; Tomography; Coherence (philosophical gambling strategy); Classification of discontinuities; Photon diffusion; Optical tomography; Physics; Light source; Computer science; Mathematics","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002846601,0.0002264445,0.0002939194,0.0002044681,0.0001009392,0.00005151976,0.00022285,0.0003301289,0.00007839247],"category_scores_gemma":[0.00008233407,0.0002237768,0.000244248,0.0008575133,0.00003331019,0.0001252918,0.00001108966,0.0005462192,0.000009646948],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006651326,"about_ca_system_score_gemma":0.00001833957,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001457765,"about_ca_topic_score_gemma":0.00005419544,"domain_scores_codex":[0.9988833,0.00002123709,0.0002297511,0.0002748125,0.0002068877,0.0003840296],"domain_scores_gemma":[0.9991846,0.0002430557,0.0000599773,0.0003239135,0.00009893232,0.00008950148],"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":[0.00008241821,0.00007529891,0.007723466,0.0002084809,0.001376777,0.000002850607,0.0007909866,0.9285716,0.05074066,0.00123241,0.001322916,0.00787207],"study_design_scores_gemma":[0.0004213751,0.00001997792,0.002683541,0.00001274004,0.0004641602,0.000003168902,0.00003284326,0.9704013,0.01805264,0.0002853339,0.007338955,0.0002839558],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3863206,0.0021442,0.6017179,0.00003035056,0.0009116543,0.0005774024,0.00007528696,0.0006732899,0.007549299],"genre_scores_gemma":[0.9898059,0.00003245622,0.009672862,0.0001814155,0.00004479803,0.00005665523,0.00009315922,0.00004581427,0.00006698999],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6034853,"threshold_uncertainty_score":0.9125353,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.002778446785827868,"score_gpt":0.2030322082103418,"score_spread":0.200253761424514,"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."}}