{"id":"W2056176441","doi":"10.1364/boe.5.004171","title":"Simultaneous decomposition of multiple hyperspectral data sets: signal recovery of unknown fluorophores in the retinal pigment epithelium","year":2014,"lang":"en","type":"article","venue":"Biomedical Optics Express","topic":"Retinal Development and Disorders","field":"Biochemistry, Genetics and Molecular Biology","cited_by":26,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Eye Institute; Deutsche Forschungsgemeinschaft; Foundation Fighting Blindness; Research to Prevent Blindness","keywords":"Hyperspectral imaging; Autofluorescence; Fluorophore; Retinal pigment epithelium; Matrix decomposition; Non-negative matrix factorization; Fluorescence; Wavelength; Optics; Biological system; Materials science; Physics; Artificial intelligence; Computer science; Retina; Biology","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005140679,0.0006644324,0.0003419612,0.0007242937,0.0002102486,0.0003395579,0.0002334682,0.0003350194,0.0007507152],"category_scores_gemma":[0.001106318,0.0002050953,0.0006088098,0.0005115757,0.0003943489,0.0004969984,0.0005550169,0.000568416,0.000219035],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002480326,"about_ca_system_score_gemma":0.0003874486,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001570845,"about_ca_topic_score_gemma":0.002122831,"domain_scores_codex":[0.9998019,0.00003192963,0.000009420863,0.0000563236,0.00006983709,0.00003051879],"domain_scores_gemma":[0.999742,0.00008530799,0.00004549078,0.00004203492,0.00006321639,0.00002194629],"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":[0.0004057401,0.0001222926,0.002565666,0.0001416027,0.0000785417,0.0001749274,0.0002597153,0.02990787,0.8792749,0.001105029,0.0003233278,0.08564036],"study_design_scores_gemma":[0.00002031907,0.0001446845,0.02753352,0.00001830413,0.00007431174,0.0005371294,0.0001938445,0.4890683,0.4783216,0.002476282,0.001540371,0.00007138521],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7049547,0.0002255089,0.293056,0.0001519809,0.00001729811,0.0000482535,0.0002939978,0.0004019755,0.0008502609],"genre_scores_gemma":[0.7956605,0.0002982428,0.2021918,0.00004285141,0.0000149693,0.00007460806,0.0005372513,0.0001519782,0.001027775],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001570845,"threshold_uncertainty_score":0.003123343,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01155418953702896,"score_gpt":0.262632818924344,"score_spread":0.251078629387315,"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."}}