{"id":"W2034528613","doi":"10.1117/12.831790","title":"4D in vivo imaging of subpleural lung parenchyma by swept source optical coherence tomography","year":2009,"lang":"en","type":"article","venue":"Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE","topic":"Optical Coherence Tomography Applications","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"St. Michael's Hospital","funders":"Deutsche Forschungsgemeinschaft","keywords":"Optical coherence tomography; Tomography; Preclinical imaging; Biomedical engineering; Lung; Parenchyma; In vivo; Doppler effect; Optical tomography; Materials science; Medicine; Radiology; Pathology; Physics; Biology","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":[0.0002710701,0.000375444,0.0001925786,0.0005690088,0.0001065984,0.0003399881,0.0002671026,0.0005195789,0.0008056766],"category_scores_gemma":[0.0002957007,0.0002602093,0.000175389,0.0001813974,0.0002327993,0.0005029349,0.0002954426,0.0003221821,0.0001805167],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001427609,"about_ca_system_score_gemma":0.000210618,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004597538,"about_ca_topic_score_gemma":0.0007959612,"domain_scores_codex":[0.9998896,0.00002797305,0.000007121596,0.00001905812,0.00004165262,0.00001463868],"domain_scores_gemma":[0.9998128,0.00006623974,0.0000401474,0.00003243861,0.00003015672,0.00001828398],"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":[0.00004339598,0.000015908,0.000315912,0.00002483933,0.000003861671,0.00006277052,0.00001233247,0.0005558291,0.9953725,0.00009358874,0.00003228084,0.003466752],"study_design_scores_gemma":[0.00002967636,0.0004539446,0.006484689,0.00001535924,0.00003208185,0.00111354,0.00004648806,0.03075132,0.9582096,0.0002845063,0.002551056,0.00002763122],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5874382,0.001853013,0.4075796,0.0003096523,0.00004476637,0.000158246,0.0003437765,0.0005949918,0.001677809],"genre_scores_gemma":[0.6662623,0.001491343,0.329944,0.0001434269,0.00003948715,0.0002095457,0.0001885386,0.00006617913,0.001655274],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0008056766,"threshold_uncertainty_score":0.002695262,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006773244129782982,"score_gpt":0.2193580902499773,"score_spread":0.2125848461201944,"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."}}