{"id":"W2034688000","doi":"10.1117/12.839593","title":"Optical modeling of a line-scan optical coherence tomography system for high-speed three-dimensional endoscopic imaging","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":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"","keywords":"Optics; Optical coherence tomography; Transverse plane; Scanner; Lens (geology); Rotation (mathematics); Distortion (music); Physics; Image quality; Optical axis; Mirror image; Line (geometry); Computer science; Image (mathematics); Computer vision; Optoelectronics","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0006241261,0.0005539755,0.0008140683,0.0002675469,0.0001191745,0.0001223958,0.001223258,0.0002835126,0.000008251487],"category_scores_gemma":[0.0003037968,0.0005069154,0.0009647929,0.0007665319,0.0003422223,0.0005278225,0.0001202172,0.0004996339,0.000001429398],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002022972,"about_ca_system_score_gemma":0.00005184975,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001159778,"about_ca_topic_score_gemma":5.101644e-7,"domain_scores_codex":[0.996385,3.568836e-8,0.00133618,0.0005950705,0.0009433319,0.0007403663],"domain_scores_gemma":[0.9968028,0.0003167379,0.0002935665,0.000151103,0.002159935,0.0002758433],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001008731,0.0001683045,0.0002320453,0.0008480271,0.0003750547,1.520495e-7,0.00005235149,0.01549021,0.4293085,0.5524165,0.0002681176,0.0007399136],"study_design_scores_gemma":[0.001534073,0.0004532863,0.0007228797,0.000775103,0.0003490806,0.00001572068,0.000387623,0.8395729,0.1501884,0.005400861,0.00005611538,0.0005438901],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9901407,0.0002505848,0.004632792,0.001004767,0.000261439,0.001380014,0.0001287179,0.0003581888,0.001842756],"genre_scores_gemma":[0.7893001,0.00001450813,0.210053,0.00003340308,0.0002693858,0.0002313906,0.00001703612,0.00007248118,0.000008700826],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8240827,"threshold_uncertainty_score":0.9997382,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0117486318627935,"score_gpt":0.2254477318778047,"score_spread":0.2136991000150112,"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."}}