{"id":"W1977934683","doi":"10.1016/j.jmr.2007.05.008","title":"Robust baseline correction algorithm for signal dense NMR spectra","year":2007,"lang":"en","type":"article","venue":"Journal of Magnetic Resonance","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":44,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Alberta; Chenomx (Canada)","funders":"","keywords":"Baseline (sea); Spectral line; NMR spectra database; SIGNAL (programming language); Algorithm; Spectrometer; Computer science; Suite; Line (geometry); Mathematics; Physics; Optics; Geology; Geometry","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002085782,0.001664797,0.001259192,0.001915412,0.001016747,0.001308664,0.002069871,0.001466419,0.005193314],"category_scores_gemma":[0.005631506,0.0008117742,0.001077809,0.00229848,0.0006182483,0.001496195,0.001973388,0.002291682,0.003207331],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006586516,"about_ca_system_score_gemma":0.002717596,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004673743,"about_ca_topic_score_gemma":0.009886602,"domain_scores_codex":[0.9986793,0.0002191385,0.00009387638,0.0003170308,0.0005581804,0.000132366],"domain_scores_gemma":[0.9980426,0.0005126873,0.0001723348,0.0004302238,0.0007695915,0.00007266115],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0004150416,0.0001617996,0.0005368933,0.000220685,0.0001466991,0.00007174638,0.00008115305,0.02735877,0.08004968,0.005947555,0.007296816,0.8777132],"study_design_scores_gemma":[0.0000738592,0.0001631546,0.002287171,0.000030764,0.0001231758,0.0003392218,0.00005037293,0.8718285,0.09810617,0.01079676,0.01612892,0.00007188631],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.004272805,0.000263324,0.9927284,0.00007359242,0.00004714467,0.00003908866,0.0001757374,0.002101043,0.000299009],"genre_scores_gemma":[0.03213202,0.00019724,0.9641716,0.00006815243,0.00004151241,0.00009133738,0.0009452355,0.0004223174,0.00193057],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005193314,"threshold_uncertainty_score":0.01737338,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01785327268042299,"score_gpt":0.2631297078511102,"score_spread":0.2452764351706872,"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."}}