{"id":"W2116327364","doi":"10.1117/1.jbo.18.4.046011","title":"Widefield quantitative multiplex surface enhanced Raman scattering imaging<i>in vivo</i>","year":2013,"lang":"en","type":"article","venue":"Journal of Biomedical Optics","topic":"Gold and Silver Nanoparticles Synthesis and Applications","field":"Materials Science","cited_by":56,"is_retracted":false,"has_abstract":true,"ca_institutions":"University Health Network; Ontario Institute for Cancer Research; University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Institutes of Health Research","keywords":"Preclinical imaging; Raman scattering; Molecular imaging; Multiplex; Materials science; In vivo; Optics; Biological imaging; Spectral imaging; Raman spectroscopy; Biomedical engineering; Fluorescence; Physics; Medicine; Bioinformatics","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":[0.001372739,0.001126937,0.0004113445,0.0006963836,0.0002396291,0.000703442,0.0005151149,0.0004735504,0.001798148],"category_scores_gemma":[0.0004566325,0.0003987869,0.0003104622,0.000315182,0.0005465752,0.0006379654,0.0004684171,0.000573785,0.0008353652],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004956484,"about_ca_system_score_gemma":0.0002827246,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004864861,"about_ca_topic_score_gemma":0.0008810024,"domain_scores_codex":[0.9994928,0.0001089357,0.00002199731,0.0001760551,0.000158419,0.00004170997],"domain_scores_gemma":[0.9996909,0.0001038796,0.00007552576,0.00004415225,0.00005568024,0.00002985659],"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":[0.00003580579,0.00001038662,0.00008851266,0.00002307556,0.000003953689,0.0000148619,0.00001406774,0.0001786415,0.9963894,0.000151308,0.00006231024,0.003027759],"study_design_scores_gemma":[0.000004000133,0.0000726885,0.0004621693,0.000002944162,0.000007169904,0.0001410722,0.000009098952,0.002825144,0.995299,0.00008876109,0.001079195,0.000008683845],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5087072,0.003934344,0.4776426,0.0004513915,0.0001681965,0.0002900183,0.0006697886,0.002402856,0.005733574],"genre_scores_gemma":[0.5747521,0.003430749,0.4096395,0.000234912,0.0001029623,0.0003676653,0.0005896905,0.0002049457,0.01067745],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001798148,"threshold_uncertainty_score":0.007259846,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0142999820841779,"score_gpt":0.2615831316096522,"score_spread":0.2472831495254743,"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."}}