{"id":"W2082165590","doi":"10.1117/1.jbo.20.2.026002","title":"Macroscopic optical imaging technique for wide-field estimation of fluorescence depth in optically turbid media for application in brain tumor surgical guidance","year":2015,"lang":"en","type":"article","venue":"Journal of Biomedical Optics","topic":"Optical Imaging and Spectroscopy Techniques","field":"Medicine","cited_by":28,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto; Ontario Institute for Cancer Research; Polytechnique Montréal","funders":"National Institute of Neurological Disorders and Stroke; National Cancer Institute; Natural Sciences and Engineering Research Council of Canada; National Institutes of Health","keywords":"Fluorescence; Attenuation; Materials science; Optics; Diffuse optical imaging; Microscopy; Microscope; Wavelength; Fluorescence-lifetime imaging microscopy; Biomedical engineering; Optoelectronics; Physics; Tomography; 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":[0.0003608199,0.0004555423,0.0002638866,0.0005108772,0.0001894481,0.000324632,0.0003346013,0.0003560924,0.0005333953],"category_scores_gemma":[0.0005419416,0.0002572217,0.000119582,0.0003054212,0.0004022227,0.0004494302,0.0004696673,0.0009073802,0.0001800574],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003231369,"about_ca_system_score_gemma":0.0005142733,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007500609,"about_ca_topic_score_gemma":0.00155844,"domain_scores_codex":[0.9998606,0.00002096171,0.000004875048,0.00003566523,0.00006666359,0.00001123348],"domain_scores_gemma":[0.9996196,0.0001186833,0.0001049026,0.00005262844,0.00007439828,0.0000298627],"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":[0.00003241957,0.00001318273,0.0005134714,0.00007709365,0.000004463036,0.0000442612,0.00003202754,0.0005160583,0.9789781,0.0007238943,0.0001297755,0.01893532],"study_design_scores_gemma":[0.00002046564,0.0005378526,0.01046638,0.00003057786,0.00005346989,0.001948273,0.00008935566,0.02804466,0.9508743,0.0007587735,0.007108269,0.00006754073],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1281764,0.002212739,0.8666175,0.0001784333,0.00005773753,0.00009210993,0.0001481158,0.0007266568,0.001790261],"genre_scores_gemma":[0.4401412,0.002397902,0.5552455,0.0001190556,0.000034969,0.0001475839,0.0001312468,0.00006824387,0.001714308],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.0007500609,"threshold_uncertainty_score":0.002344489,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01535650664055652,"score_gpt":0.3477436971665097,"score_spread":0.3323871905259531,"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."}}