{"id":"W2166581432","doi":"10.1039/c1an15719a","title":"Adaptive multiscale regression for reliable Raman quantitative analysis","year":2011,"lang":"en","type":"article","venue":"The Analyst","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada; Higher Education Discipline Innovation Project","keywords":"Raman spectroscopy; Robustness (evolution); Calibration; Preprocessor; Computer science; Wavelet; Biological system; Artificial intelligence; Algorithm; Mathematics; Chemistry; Physics; Optics; Statistics","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":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0002542825,0.0001996478,0.0004542351,0.0002619512,0.0003132784,0.00002776875,0.0004379952,0.0001005589,0.002394632],"category_scores_gemma":[0.00008329217,0.0001223857,0.00050702,0.001815778,0.000136371,0.00009909377,0.00006376471,0.0001632727,0.00005371237],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005761443,"about_ca_system_score_gemma":0.00002159651,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000995533,"about_ca_topic_score_gemma":0.000222812,"domain_scores_codex":[0.9987997,0.00002448788,0.0002967412,0.0003613916,0.000205674,0.0003119877],"domain_scores_gemma":[0.9986585,0.0002446279,0.000249809,0.0006115493,0.0001558872,0.00007966109],"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":[0.009781348,0.005020976,0.2846178,0.0006847409,0.1319788,0.0001253663,0.04626936,0.00365061,0.3512058,0.09163345,0.06835239,0.006679413],"study_design_scores_gemma":[0.001869604,0.0003552478,0.007001233,0.00006597376,0.03698519,0.000005324568,0.02286651,0.07099805,0.8456919,0.006928675,0.006093998,0.001138358],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7273962,0.004369225,0.09118495,0.0004351337,0.0001041157,0.0003762118,0.0002153546,0.0003425119,0.1755763],"genre_scores_gemma":[0.9777297,0.00006211287,0.008287758,0.00006618363,0.00006041764,0.00005028642,0.00005394687,0.00002040931,0.01366913],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.494486,"threshold_uncertainty_score":0.9985173,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08548781346411628,"score_gpt":0.3227171726969616,"score_spread":0.2372293592328453,"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."}}