{"id":"W2799914770","doi":"10.1002/jrs.5389","title":"Biochemical fingerprint of colorectal cancer cell lines using label‐free live single‐cell Raman spectroscopy","year":2018,"lang":"en","type":"article","venue":"Journal of Raman Spectroscopy","topic":"Spectroscopy Techniques in Biomedical and Chemical Research","field":"Biochemistry, Genetics and Molecular Biology","cited_by":46,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Health Services and Delivery Research Programme; National Institute for Health and Care Research; University of Leeds; Engineering and Physical Sciences Research Council; Medical Research Council Canada; Medical Research Council","keywords":"Raman spectroscopy; Principal component analysis; Linear discriminant analysis; Chemistry; Cell culture; HL60; Cell; Analytical Chemistry (journal); Biology; Biochemistry; Chromatography; Artificial intelligence; Genetics; Computer science; Optics","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0004909618,0.0004034227,0.0006478467,0.0001484831,0.0001142629,0.0000609512,0.001182649,0.0004603136,0.0005776547],"category_scores_gemma":[0.0003137171,0.0003431458,0.000340703,0.0003248483,0.0009708814,0.00002131352,0.0005263108,0.0006045527,0.00000746053],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003282282,"about_ca_system_score_gemma":0.0004403973,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00009348425,"about_ca_topic_score_gemma":0.00002524803,"domain_scores_codex":[0.9968538,0.00009402278,0.0009497883,0.0005140614,0.000814424,0.000773847],"domain_scores_gemma":[0.9975421,0.00006678588,0.0007557645,0.0005620325,0.00067208,0.0004012033],"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":[0.00117023,0.0007973296,0.0009333617,0.00008966293,0.00009027456,0.00001792038,0.00008074889,0.000001202922,0.981515,0.00002669191,0.01520764,0.00006989611],"study_design_scores_gemma":[0.001457247,0.004706062,0.00009189931,0.0001614782,0.0000852912,0.00006647438,0.00009737908,0.0001303241,0.9890987,0.0006219671,0.003142308,0.0003409123],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9879214,0.00316568,0.004913248,0.0002655287,0.0004448953,0.0002276226,0.00004220289,0.00002332139,0.002996078],"genre_scores_gemma":[0.9013832,0.001724184,0.0941692,0.0001980102,0.002227224,0.000008533689,0.00001362654,0.00007060247,0.0002054827],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.08925595,"threshold_uncertainty_score":0.9999021,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01735853541634299,"score_gpt":0.332814071743248,"score_spread":0.315455536326905,"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."}}