{"id":"W4295096973","doi":"10.1364/ol.468707","title":"Image-based cross-calibration method for multiple spectrometer-based OCT","year":2022,"lang":"en","type":"article","venue":"Optics Letters","topic":"Optical Coherence Tomography Applications","field":"Engineering","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Canadian Institutes of Health Research; Natural Sciences and Engineering Research Council of Canada; Alzheimer Society Research Program; Canada Foundation for Innovation","keywords":"Optics; Spectrometer; Optical coherence tomography; Calibration; Coherence (philosophical gambling strategy); Pixel; Imaging spectrometer; Cross-correlation; Physics; Wavelength; Computer science; Mathematics","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002305861,0.000161589,0.0001461325,0.0001659165,0.0002235415,0.0001231565,0.0002469079,0.00003636664,0.0001424804],"category_scores_gemma":[0.0000437726,0.0001948212,0.0001567041,0.0004275365,0.00005797737,0.0001099661,0.00002424295,0.0002188685,0.000008725707],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001082672,"about_ca_system_score_gemma":0.00002589039,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000006077576,"about_ca_topic_score_gemma":0.000001651666,"domain_scores_codex":[0.9989604,0.00003800732,0.0002309199,0.0002467403,0.0002049543,0.0003189594],"domain_scores_gemma":[0.9990371,0.0004537433,0.00003961589,0.0003587271,0.00003313414,0.00007767664],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00003078623,0.00007719712,0.0003445413,0.00006791622,0.00003363386,0.000002940001,0.00002451888,0.5104133,0.4856184,0.000769117,0.002025083,0.0005925202],"study_design_scores_gemma":[0.0006897404,0.00007478293,0.0002964971,0.000002389936,0.00002786573,8.650474e-7,0.000009734644,0.9654734,0.03035686,0.0001764123,0.002661375,0.000230062],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.05616514,0.00000917336,0.9405216,0.001677725,0.000157761,0.0005757763,0.0001821513,0.0003829971,0.0003276862],"genre_scores_gemma":[0.3620007,2.336105e-7,0.6360275,0.0009174052,0.0000526991,0.0008025452,0.0001416227,0.00004837829,0.000008856343],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.4552616,"threshold_uncertainty_score":0.7944576,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01469142866463977,"score_gpt":0.2719163397862097,"score_spread":0.2572249111215699,"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."}}