{"id":"W1980034664","doi":"10.1364/oe.19.010747","title":"Multi-modality optical neural imaging using coherence control of VCSELs","year":2011,"lang":"en","type":"article","venue":"Optics Express","topic":"Optical Imaging and Spectroscopy Techniques","field":"Medicine","cited_by":19,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"Networks of Centres of Excellence of Canada; Stryker; Natural Sciences and Engineering Research Council of Canada; National Defense Science and Engineering Graduate; University of California, San Francisco; University of Toronto","keywords":"Optical coherence tomography; Optics; Laser; Coherence (philosophical gambling strategy); Materials science; Preclinical imaging; Continuous wave; Blood flow; Semiconductor laser theory; In vivo; Physics; Medicine","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":[0.0002649867,0.0002095983,0.0001278357,0.0002623886,0.0001030955,0.0003191385,0.0002465483,0.0002532363,0.0005653028],"category_scores_gemma":[0.0004906132,0.00009819712,0.00009301802,0.0001978727,0.0002991696,0.0003890221,0.0002698077,0.0001888403,0.00005902938],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003690817,"about_ca_system_score_gemma":0.0001696498,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006194937,"about_ca_topic_score_gemma":0.001618738,"domain_scores_codex":[0.9998651,0.00002472723,0.00000735444,0.00003801536,0.00004658743,0.00001818701],"domain_scores_gemma":[0.9997692,0.0001124859,0.0000486707,0.00001560893,0.00003867214,0.0000154683],"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":[0.00009462004,0.00001655634,0.0006060374,0.00004355759,0.000007033379,0.00004332621,0.00003456718,0.0006929509,0.9850845,0.0004247752,0.00008966831,0.01286238],"study_design_scores_gemma":[0.00002774966,0.0003360546,0.006132281,0.00001463934,0.00002552212,0.0004001434,0.00003286488,0.03154472,0.9591964,0.0007406759,0.00152354,0.00002539131],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.896494,0.001596634,0.09846622,0.0002353649,0.00003007862,0.00005693637,0.0001785937,0.000256738,0.002685569],"genre_scores_gemma":[0.9621184,0.000403052,0.03636681,0.00006321506,0.00001356864,0.0000526946,0.00006096234,0.00002230596,0.000898999],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0006194937,"threshold_uncertainty_score":0.002677917,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05488124602444644,"score_gpt":0.3407795566539045,"score_spread":0.285898310629458,"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."}}