{"id":"W1992263674","doi":"10.1364/oe.21.001163","title":"Numerical compensation of system polarization mode dispersion in polarization-sensitive optical coherence tomography","year":2013,"lang":"en","type":"article","venue":"Optics Express","topic":"Optical Coherence Tomography Applications","field":"Engineering","cited_by":43,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Ottawa","funders":"National Center for Research Resources; National Cancer Institute; National Institutes of Health; National Institute of Biomedical Imaging and Bioengineering; National Research Foundation of Korea; National Research Foundation","keywords":"Optics; Optical coherence tomography; Polarization mode dispersion; Birefringence; Polarization (electrochemistry); Optical circulator; Materials science; Optical fiber; Multi-mode optical fiber; Physics","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":[0.00008018736,0.0001935826,0.0002701355,0.0002537369,0.00005298118,0.00005779673,0.0001998284,0.0001700985,0.00001909159],"category_scores_gemma":[0.00002714598,0.0002070419,0.00007279727,0.0008802529,0.0001083165,0.0003457365,0.00003467113,0.0002167637,0.00003406209],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006968353,"about_ca_system_score_gemma":0.00001510003,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001613778,"about_ca_topic_score_gemma":0.000005231502,"domain_scores_codex":[0.9986632,0.00004781673,0.0004612731,0.0002494311,0.0002959947,0.0002823254],"domain_scores_gemma":[0.9990958,0.0001466398,0.00007120235,0.000329127,0.000229039,0.0001282173],"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":[0.00002473811,0.0003230306,0.0430281,0.0004909817,0.00007654999,0.000003819538,0.0007981546,0.1843905,0.6407804,0.1280144,0.00004595471,0.002023266],"study_design_scores_gemma":[0.0003889645,0.00005352087,0.05808944,0.0002211408,0.00002929528,0.000003933116,0.0004361323,0.8977963,0.04223535,0.0003928767,0.000008970288,0.0003441171],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7104774,0.00003973308,0.2824782,0.00003919696,0.0001029104,0.000795756,0.00002733933,0.0002426826,0.005796791],"genre_scores_gemma":[0.9766482,0.000006086414,0.0230784,0.000008351369,0.00002439798,0.0001087332,0.000083133,0.00003218257,0.00001052252],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7134057,"threshold_uncertainty_score":0.8442925,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00781909267200622,"score_gpt":0.2154700270372329,"score_spread":0.2076509343652267,"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."}}