{"id":"W4416257031","doi":"10.1364/ol.569766","title":"Enhanced visualization of ex-vivo ocular tissues using spatial-phase-resolved optical coherence microscopy","year":2025,"lang":"en","type":"article","venue":"Optics Letters","topic":"Optical Coherence Tomography Applications","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Simon Fraser University; University of British Columbia","funders":"Japan Society for the Promotion of Science; Canadian Institutes of Health Research; Natural Sciences and Engineering Research Council of Canada; Alzheimer Society Research Program; Core Research for Evolutional Science and Technology; Canadian Cancer Society","keywords":"Interferometry; Visualization; Coherence (philosophical gambling strategy); Numerical aperture; Optical coherence tomography; Focus (optics); Microscopy; Phase (matter); Phase retrieval; Wavefront","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":[0.0002467355,0.0003280919,0.0001627746,0.0003054864,0.0001380735,0.0004863328,0.0002655132,0.0002957845,0.0005293374],"category_scores_gemma":[0.0004620674,0.0001762574,0.0001045943,0.0002471181,0.0002958581,0.0005617143,0.0004783264,0.0003579183,0.0001010939],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002966452,"about_ca_system_score_gemma":0.0004742866,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001008515,"about_ca_topic_score_gemma":0.002706516,"domain_scores_codex":[0.9999012,0.00002519476,0.000005194732,0.00001825908,0.00003442389,0.00001571658],"domain_scores_gemma":[0.9998094,0.00009699785,0.00003653526,0.00002079675,0.00002297722,0.00001313267],"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":[0.00004191574,0.0000148229,0.0002966872,0.00008431129,0.000006654321,0.0001203638,0.00004795893,0.003061868,0.9882216,0.0009009527,0.0001480381,0.007054846],"study_design_scores_gemma":[0.00003524829,0.0002010872,0.00560381,0.00003376256,0.00003257344,0.0007503651,0.00008602388,0.1835206,0.8037871,0.001460355,0.00444748,0.0000415082],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7051321,0.003109301,0.2880079,0.0004868404,0.00003748936,0.00007423398,0.0002468641,0.0004769421,0.002428248],"genre_scores_gemma":[0.8232778,0.001837797,0.1730139,0.0001417075,0.00002965804,0.00007699851,0.000168407,0.00008566245,0.001368125],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001008515,"threshold_uncertainty_score":0.002152383,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01228173394863155,"score_gpt":0.3011405850413913,"score_spread":0.2888588510927598,"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."}}