{"id":"W2757020358","doi":"10.1177/0003702817732117","title":"Empirical Factors Affecting the Quality of Non-Negative Matrix Factorization of Mammalian Cell Raman Spectra","year":2017,"lang":"en","type":"article","venue":"Applied Spectroscopy","topic":"Spectroscopy Techniques in Biomedical and Chemical Research","field":"Biochemistry, Genetics and Molecular Biology","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"Canada's Michael Smith Genome Sciences Centre; University of British Columbia","funders":"British Columbia Knowledge Development Fund; Natural Sciences and Engineering Research Council of Canada","keywords":"Raman spectroscopy; Interpretability; Principal component analysis; Biological system; Hyperspectral imaging; Non-negative matrix factorization; Macromolecule; Matrix (chemical analysis); Spectral line; Chemistry; Matrix decomposition; Computer science; Analytical Chemistry (journal); Artificial intelligence; Optics; Physics; Biology; Chromatography; Biochemistry; Eigenvalues and eigenvectors","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004969901,0.000220755,0.000344363,0.00004040285,0.0002471728,0.00004097874,0.0008614393,0.0002477805,0.00007965545],"category_scores_gemma":[0.0003415988,0.0001540013,0.0001656998,0.000110283,0.000711616,0.000006028747,0.0003122239,0.0003030685,0.000003455824],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004301716,"about_ca_system_score_gemma":0.00008471451,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001596663,"about_ca_topic_score_gemma":0.0000200445,"domain_scores_codex":[0.9982469,0.00005972595,0.0004322065,0.0004112136,0.0004676141,0.0003823274],"domain_scores_gemma":[0.9983267,0.0001184883,0.0004166309,0.0009424402,0.00008407694,0.0001117061],"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":[0.0001744566,0.0001244884,0.01502073,0.0000685362,0.00003012212,3.44365e-7,0.0001442952,6.72041e-7,0.9833952,0.0003993022,0.0005937308,0.00004814622],"study_design_scores_gemma":[0.0004105663,0.0002922533,0.03812357,0.00001080572,0.00001375298,3.610054e-7,0.0002178374,0.000006572435,0.9588949,0.001648474,0.0002260836,0.0001548004],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.958775,0.00003255645,0.0262057,0.0001355174,0.00007040361,0.0004056701,0.00003357104,0.00001684171,0.01432478],"genre_scores_gemma":[0.9944618,0.00006755572,0.004938638,0.00002701943,0.0001870153,0.00001778908,0.00004732121,0.00002560583,0.0002272404],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03568685,"threshold_uncertainty_score":0.6279991,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02715222054972019,"score_gpt":0.3999208150506032,"score_spread":0.372768594500883,"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."}}