{"id":"W4389584442","doi":"10.1364/fio.2023.fm6c.3","title":"Artifact suppression and improved SNR by phase-locked multiplexed coherent imaging","year":2023,"lang":"en","type":"article","venue":"","topic":"Optical Coherence Tomography Applications","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Queen's University","funders":"","keywords":"Multiplexing; Computer science; Autocorrelation; Signal processing; Phase (matter); Artifact (error); Computer vision; Artificial intelligence; Physics; Telecommunications; Digital signal processing; Computer hardware; Mathematics","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003950583,0.0004579341,0.000190685,0.0003621717,0.0001528386,0.0005167774,0.0004313466,0.0003184164,0.001394096],"category_scores_gemma":[0.0009267331,0.0002172586,0.0001020679,0.0003988557,0.000478027,0.0007390421,0.000430419,0.0004449202,0.0004137217],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000238286,"about_ca_system_score_gemma":0.0003885595,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002347219,"about_ca_topic_score_gemma":0.0005319529,"domain_scores_codex":[0.9997702,0.0000429063,0.00001573305,0.0000495057,0.00009225416,0.00002942942],"domain_scores_gemma":[0.9996258,0.0001277355,0.0001175831,0.00004143742,0.00006215862,0.00002520921],"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":[0.00008040266,0.00002749498,0.0001786072,0.00002507776,0.000002637262,0.00003687989,0.00003207724,0.0004577869,0.9854393,0.0008954386,0.0001243789,0.01270009],"study_design_scores_gemma":[0.00001435528,0.00008002848,0.0002917871,0.000003162891,0.000003470829,0.0001123197,0.000005568719,0.008944308,0.9895271,0.0002398217,0.0007716703,0.000006406483],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7292478,0.0009442198,0.2638969,0.0003407811,0.00006041168,0.0000861277,0.0001264148,0.001322698,0.003974666],"genre_scores_gemma":[0.7782953,0.0004804685,0.21798,0.0002239173,0.00005809615,0.00007471206,0.0001373452,0.0002294791,0.002520579],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001394096,"threshold_uncertainty_score":0.004663706,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00890455320104199,"score_gpt":0.2531399746186184,"score_spread":0.2442354214175764,"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."}}