{"id":"W2007531982","doi":"10.1364/ol.38.002572","title":"Imaging the electro-kinetic response of biological tissues with optical coherence tomography","year":2013,"lang":"en","type":"article","venue":"Optics Letters","topic":"Optical Coherence Tomography Applications","field":"Engineering","cited_by":14,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"Natural Sciences and Engineering Research Council of Canada; Mitacs","keywords":"Optical coherence tomography; Optics; Electric field; Amplitude; Coherence (philosophical gambling strategy); Optical tomography; Materials science; Kinetic energy; Tomography; SIGNAL (programming language); Preclinical imaging; Image quality; Biological imaging; Physics; In vivo; Computer science; Image (mathematics); Fluorescence","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0001577261,0.0001880383,0.0001793678,0.0001033472,0.00006147279,0.00006772293,0.0003673504,0.00004920021,0.00008593198],"category_scores_gemma":[0.00003619757,0.0001218078,0.00007017533,0.0005944537,0.0005480846,0.00009238744,0.00003311211,0.0002555653,0.0000526933],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001633776,"about_ca_system_score_gemma":0.00001026101,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001042014,"about_ca_topic_score_gemma":7.713813e-7,"domain_scores_codex":[0.9989324,0.00005131121,0.0002368852,0.0002001388,0.0001985496,0.0003807477],"domain_scores_gemma":[0.9989515,0.0004155628,0.00003618108,0.0004298412,0.00007329012,0.00009364883],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.00007772665,0.00006974661,0.01167544,0.00002962684,0.0001060782,0.000009649889,0.0001790482,0.003267581,0.972874,0.007346152,0.00150147,0.00286343],"study_design_scores_gemma":[0.001855973,0.001338432,0.5857704,0.00027574,0.0003255948,0.0001818529,0.0008593907,0.07576542,0.3225142,0.003681442,0.004907055,0.00252447],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9815038,0.000171139,0.01320118,0.002761407,0.00003096242,0.0004606052,0.00000430527,0.0001990323,0.001667582],"genre_scores_gemma":[0.9776179,0.00001770546,0.02176439,0.0003464292,0.00002807994,0.0001911556,0.000003290006,0.00002470091,0.00000634328],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6503598,"threshold_uncertainty_score":0.4967177,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008286826838358761,"score_gpt":0.2104314380395668,"score_spread":0.202144611201208,"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."}}