{"id":"W2885133095","doi":"10.1177/0003702818794957","title":"Smoothing Raman Spectra with Contiguous Single-Channel Fitting of Voigt Distributions: An Automated, High-Quality Procedure","year":2018,"lang":"en","type":"article","venue":"Applied Spectroscopy","topic":"Spectroscopy Techniques in Biomedical and Chemical Research","field":"Biochemistry, Genetics and Molecular Biology","cited_by":8,"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; Canadian Institutes of Health Research","keywords":"Smoothing; Residual; Spectral line; Mathematics; Gaussian; Noise (video); Limit (mathematics); Algorithm; Computational physics; Optics; Physics; Statistics; Mathematical analysis; Computer science; Artificial intelligence","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0004227464,0.0003094719,0.0003695298,0.00006475568,0.0001946328,0.00005511399,0.0005089582,0.0003166677,0.000090006],"category_scores_gemma":[0.0001483509,0.0002586538,0.00007499014,0.0003213335,0.0009173712,0.00001151446,0.0001708767,0.0003131064,0.00001087555],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009437634,"about_ca_system_score_gemma":0.0001367786,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006623664,"about_ca_topic_score_gemma":0.00004390282,"domain_scores_codex":[0.9975824,0.00004921365,0.0004665486,0.0007344998,0.0004847626,0.0006825915],"domain_scores_gemma":[0.9986471,0.00003367339,0.0002197177,0.0006571735,0.0001960712,0.000246283],"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":[0.0005314911,0.0003802649,0.0001785117,0.00005412657,0.00005796687,0.000002335236,0.0000550964,8.815253e-7,0.9924847,0.004649622,0.001341209,0.0002637992],"study_design_scores_gemma":[0.0007447708,0.00179517,0.000635036,0.00004901902,0.00002278344,0.0000130788,0.0001060099,0.00008898805,0.9928713,0.002064384,0.001277847,0.0003315881],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9534997,0.0001019536,0.03823971,0.0002618104,0.0000509587,0.0004856843,0.00007115825,0.0003523871,0.006936654],"genre_scores_gemma":[0.9627172,0.00003516379,0.03577257,0.0002016617,0.0007340012,0.00008134537,0.0003216703,0.00004811236,0.00008826572],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.009217525,"threshold_uncertainty_score":0.9999866,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01441548853862989,"score_gpt":0.3317026470198776,"score_spread":0.3172871584812477,"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."}}