{"id":"W2614953272","doi":"10.1016/j.saa.2017.05.038","title":"An improved partial least-squares regression method for Raman spectroscopy","year":2017,"lang":"en","type":"article","venue":"Spectrochimica Acta Part A Molecular and Biomolecular Spectroscopy","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":10,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Partial least squares regression; Sorting; Feature selection; Mathematics; Regression analysis; Genetic algorithm; Limit (mathematics); Statistics; Mean squared error; Selection (genetic algorithm); Regression; Computer science; Algorithm; Artificial intelligence; Mathematical optimization","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":["metaepi_narrow","sts","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.0005054093,0.001200273,0.001341472,0.0003835706,0.001913917,0.001424259,0.001777829,0.0005840295,0.0007119586],"category_scores_gemma":[0.0003527446,0.001093561,0.0007508115,0.0003200769,0.0005681107,0.0005769046,0.0003591097,0.0007429579,0.00001620031],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002235417,"about_ca_system_score_gemma":0.0002114819,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001899601,"about_ca_topic_score_gemma":0.0000692598,"domain_scores_codex":[0.9942256,0.0001252001,0.0008725552,0.00224811,0.0006695401,0.001859037],"domain_scores_gemma":[0.9949715,0.00009763821,0.0008719757,0.003038316,0.0001866827,0.0008339165],"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":[0.0008844641,0.0004547608,0.0004677328,0.0001491918,0.0004847817,0.00008344387,0.00008915434,0.000001319155,0.9936785,0.002636585,0.0006060724,0.000464016],"study_design_scores_gemma":[0.002780243,0.0009616553,0.0002080467,0.00007716727,0.0009478408,0.0001110134,0.000155063,0.003505811,0.9820805,0.00326867,0.004607515,0.001296493],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6484526,0.001929832,0.3337275,0.00371642,0.0005156574,0.001209383,0.0003146184,0.0007179761,0.009415977],"genre_scores_gemma":[0.9048312,0.0003067918,0.09235928,0.0004717288,0.0009083921,0.0002304185,0.0002929564,0.0002283827,0.0003707935],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2563786,"threshold_uncertainty_score":0.9996123,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01430127634636114,"score_gpt":0.3345831321511023,"score_spread":0.3202818558047412,"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."}}