{"id":"W4388139091","doi":"10.20944/preprints202310.2032.v1","title":"Mathematical and Statistical Review of NMR Time Domain Data Pre-Processing","year":2023,"lang":"en","type":"preprint","venue":"Preprints.org","topic":"NMR spectroscopy and applications","field":"Physics and Astronomy","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University; Spinal Cord Injury BC; University of British Columbia","funders":"","keywords":"Computer science; Data processing; Weighting; Offset (computer science); Data mining; Time domain; Domain (mathematical analysis); Simple (philosophy); Algorithm; Theoretical computer science; Mathematics; Physics; Programming language","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":[0.005266792,0.001727593,0.001430448,0.003195758,0.0006286228,0.002836792,0.001868843,0.00173349,0.005267506],"category_scores_gemma":[0.01472731,0.0007743362,0.001569357,0.003445683,0.001980178,0.00307911,0.001252687,0.00302307,0.004848124],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00119663,"about_ca_system_score_gemma":0.002219192,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001564122,"about_ca_topic_score_gemma":0.0008813399,"domain_scores_codex":[0.9974229,0.0007757678,0.0003955405,0.0004748159,0.0008442423,0.00008666817],"domain_scores_gemma":[0.9917618,0.005340198,0.0004754626,0.0005502995,0.001790353,0.00008191609],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.00008580728,0.00009845624,0.0009162811,0.007746947,0.0003046496,0.000735346,0.0003507863,0.02918837,0.006751802,0.3246825,0.06987323,0.5592658],"study_design_scores_gemma":[0.00001960848,0.0001937548,0.002039505,0.002196396,0.0002221446,0.002483838,0.0001217392,0.05043272,0.007037212,0.2504236,0.6845855,0.0002440046],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"review","genre_scores_codex":[0.001300019,0.2145162,0.7669263,0.004250304,0.002796422,0.0001053423,0.0009560347,0.0005776591,0.008571762],"genre_scores_gemma":[0.02516465,0.4785214,0.4654838,0.003343972,0.01476247,0.0005721055,0.002356228,0.000850288,0.008945029],"genre_candidate":"review","genre_consensus":null,"teacher_disagreement_score":0.005267506,"threshold_uncertainty_score":0.02785379,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.125467954389488,"score_gpt":0.4468355198197192,"score_spread":0.3213675654302312,"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."}}