{"id":"W4411823307","doi":"10.1364/oe.567621","title":"Liquid lens-based endoscopic OCT probe with adjustable focus for 3D imaging in cross-sectionally irregular lumens","year":2025,"lang":"en","type":"article","venue":"Optics Express","topic":"Optical Coherence Tomography Applications","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"Liaoning Revitalization Talents Program","keywords":"Optics; Lens (geology); Focus (optics); Optical coherence tomography; Materials science; Integral imaging; Physics; Computer science","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":[0.0003955791,0.0004142399,0.0002826483,0.000406146,0.0002285313,0.0003797001,0.0005181402,0.0006927238,0.0006072932],"category_scores_gemma":[0.0007022206,0.0003062416,0.0002050988,0.0002911246,0.0004185448,0.0008908143,0.0006582682,0.0004603013,0.0002256748],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004447043,"about_ca_system_score_gemma":0.0005863735,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007971924,"about_ca_topic_score_gemma":0.001391529,"domain_scores_codex":[0.9997399,0.0000447281,0.00001938709,0.00006283705,0.0001025025,0.00003053885],"domain_scores_gemma":[0.9994742,0.0001538181,0.0001668915,0.00005383712,0.00009791166,0.00005326199],"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":[0.00006607607,0.00001782565,0.0003449055,0.00005496659,0.000003659286,0.0001385,0.00004968997,0.0004996572,0.9884963,0.0003747769,0.0001764058,0.00977712],"study_design_scores_gemma":[0.00005589342,0.0004944453,0.00288997,0.00002622474,0.00003568353,0.00167157,0.0000746495,0.05245123,0.9368854,0.0003135515,0.004997436,0.0001038634],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4341658,0.001635739,0.5598218,0.0005341482,0.00009085564,0.0001708822,0.0002076335,0.001077065,0.002296137],"genre_scores_gemma":[0.6911307,0.0004665917,0.3067921,0.0002893406,0.00003374217,0.0001634594,0.00008239384,0.0000562741,0.0009853786],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.0007971924,"threshold_uncertainty_score":0.003226638,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009130692036981431,"score_gpt":0.2493860965378031,"score_spread":0.2402554045008216,"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."}}