{"id":"W2071776737","doi":"10.1364/ao.51.001701","title":"Optimal signal processing of nonlinearity in swept-source and spectral-domain optical coherence tomography","year":2012,"lang":"en","type":"article","venue":"Applied Optics","topic":"Optical Coherence Tomography Applications","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo; National Research Council Canada","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Optics; Fourier transform; Optical coherence tomography; Convolution (computer science); Frequency domain; Bessel function; Signal processing; Computer science; Algorithm; Physics; Mathematics; Digital signal processing; Mathematical analysis; Artificial intelligence","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004238056,0.0003830257,0.0002448899,0.0002285546,0.0001554397,0.0003374743,0.0003358015,0.0003389389,0.000482446],"category_scores_gemma":[0.001710477,0.0001342877,0.0002028856,0.0003184744,0.0004088376,0.0005142537,0.0003801532,0.0003208139,0.0001450143],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002688612,"about_ca_system_score_gemma":0.0005646594,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000963301,"about_ca_topic_score_gemma":0.002054272,"domain_scores_codex":[0.9998139,0.0000448742,0.00001147654,0.00002594614,0.00008434488,0.00001949093],"domain_scores_gemma":[0.9994919,0.0002652485,0.00008647241,0.00006715129,0.00006733771,0.00002178984],"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":[0.0007078414,0.0002136975,0.002538635,0.0002006794,0.0000645546,0.0003194583,0.0002423439,0.2797642,0.5693443,0.01219055,0.0007640368,0.1336496],"study_design_scores_gemma":[0.00001808923,0.00007867883,0.001061823,0.000008425536,0.000009184623,0.000128701,0.00002047563,0.8286899,0.1675856,0.001492467,0.0008790853,0.00002758546],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3209641,0.0002162764,0.6772225,0.0001501498,0.00003180256,0.00003534307,0.00006987891,0.000263393,0.001046562],"genre_scores_gemma":[0.4874872,0.0001776063,0.5112668,0.00003623453,0.00001438185,0.00005847714,0.00009073301,0.0000632463,0.0008054261],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.000963301,"threshold_uncertainty_score":0.002241313,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01016731292029542,"score_gpt":0.2255495820169938,"score_spread":0.2153822690966984,"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."}}