{"id":"W2150645204","doi":"10.1109/tbme.2009.2032160","title":"Chemometric Approach for Improving VCSEL-Based Glucose Predictions","year":2009,"lang":"en","type":"article","venue":"IEEE Transactions on Biomedical Engineering","topic":"Spectroscopy Techniques in Biomedical and Chemical Research","field":"Biochemistry, Genetics and Molecular Biology","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia; University of British Columbia Hospital","funders":"","keywords":"Laser; Materials science; Preprocessor; Vertical-cavity surface-emitting laser; Absorption (acoustics); Partial least squares regression; Spectral line; Optoelectronics; Optics; Computer science; Biomedical engineering; Artificial intelligence; Physics; Medicine; Machine learning","routes":{"ca_aff":true,"ca_fund":false,"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":[0.0002106304,0.0002066446,0.000170988,0.0003047709,0.0001121309,0.00002441004,0.0002839646,0.0003406248,0.0000259787],"category_scores_gemma":[0.00009874957,0.0001900714,0.0001931961,0.000649097,0.0001114051,0.000005273963,0.000002650442,0.0003249467,0.000002511727],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006664283,"about_ca_system_score_gemma":0.00007990446,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00000271535,"about_ca_topic_score_gemma":1.017481e-7,"domain_scores_codex":[0.9984694,0.00001002492,0.0002513848,0.0004394697,0.0003594796,0.0004702373],"domain_scores_gemma":[0.9992118,0.00005034805,0.00002915909,0.0003073384,0.00006039426,0.0003409905],"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":[0.0001056493,0.000557791,5.233421e-7,0.00005915535,0.00002746216,0.000001153221,0.00000278186,0.001946948,0.9671311,0.00002008895,0.0008421195,0.02930518],"study_design_scores_gemma":[0.0007224179,0.001077599,0.00001231414,0.0000154614,0.00002155083,0.000007181527,0.000003424133,0.1346532,0.8495805,0.00001502714,0.01367783,0.0002134363],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.005542816,0.0001330196,0.9930944,0.0002449789,0.0001899732,0.0003342439,0.00006351201,0.0001839589,0.0002130807],"genre_scores_gemma":[0.9354587,0.00005948092,0.06328551,0.0002882145,0.0002987545,0.0002271776,0.0001198693,0.00003109366,0.000231252],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9299158,"threshold_uncertainty_score":0.7750887,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008547753673461644,"score_gpt":0.263788628150337,"score_spread":0.2552408744768754,"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."}}