{"id":"W1977568999","doi":"10.1117/1.jbo.18.9.096001","title":"Hyperspectral fluorescence lifetime imaging for optical biopsy","year":2013,"lang":"en","type":"article","venue":"Journal of Biomedical Optics","topic":"Optical and Acousto-Optic Technologies","field":"Physics and Astronomy","cited_by":32,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs; Hamilton Health Sciences; American Occupational Therapy Foundation","keywords":"Hyperspectral imaging; Photomultiplier; Optics; Fluorescence; Materials science; Fluorescence-lifetime imaging microscopy; Digital micromirror device; SIGNAL (programming language); Optical filter; Wavelength; Biomedical engineering; Optoelectronics; Computer science; Detector; Physics; Artificial intelligence; 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":[0.0006630174,0.0005450574,0.0002909567,0.000633282,0.0002381286,0.0005105413,0.0005685778,0.0007111121,0.00519525],"category_scores_gemma":[0.0007856476,0.0003017244,0.000230696,0.0003703343,0.0003009611,0.0008501638,0.0006360327,0.0006029966,0.001370813],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005016286,"about_ca_system_score_gemma":0.0004457168,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000631361,"about_ca_topic_score_gemma":0.00133769,"domain_scores_codex":[0.9996414,0.0001258922,0.00001253668,0.00005869888,0.0001357567,0.00002575602],"domain_scores_gemma":[0.9995276,0.0001749134,0.00008247112,0.00006303952,0.0001086459,0.00004331093],"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":[0.0001783803,0.0000577113,0.0004671338,0.0001909288,0.00001099411,0.00009530532,0.000028988,0.001150427,0.9370169,0.001711936,0.001338795,0.05775252],"study_design_scores_gemma":[0.00004626912,0.0004614239,0.004440689,0.0001203363,0.00003758791,0.003366664,0.00006846732,0.08669017,0.8657972,0.002537274,0.03633251,0.0001014827],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.08897506,0.009813836,0.882818,0.001461256,0.0002317696,0.0003611448,0.0005627521,0.002325791,0.01345043],"genre_scores_gemma":[0.226959,0.003993077,0.7626175,0.0006190402,0.0001064239,0.0003009745,0.0003563514,0.0001930279,0.004854555],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00519525,"threshold_uncertainty_score":0.01737988,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00655614941583031,"score_gpt":0.2392700588996937,"score_spread":0.2327139094838634,"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."}}