{"id":"W4399865220","doi":"10.47852/bonviewjdsis42022556","title":"Insights into Nuclear Magnetic Resonance Data Preprocessing: A Comprehensive Review","year":2024,"lang":"en","type":"review","venue":"Journal of Data Science and Intelligent Systems","topic":"NMR spectroscopy and applications","field":"Physics and Astronomy","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University; University of British Columbia","funders":"","keywords":"Nuclear magnetic resonance; Nuclear data; Computer science; Physics; Nuclear physics","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":[0.006503524,0.001358038,0.001879924,0.00540357,0.0005059724,0.00263082,0.001777726,0.001644695,0.003445089],"category_scores_gemma":[0.01340418,0.0006830674,0.001751294,0.005034138,0.001146191,0.003497961,0.00126341,0.002072611,0.002105845],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001111231,"about_ca_system_score_gemma":0.004852842,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002261105,"about_ca_topic_score_gemma":0.002933389,"domain_scores_codex":[0.9978829,0.0006405044,0.0004662529,0.0002992853,0.0006309213,0.00008005834],"domain_scores_gemma":[0.9877294,0.009242361,0.0007069408,0.0002808251,0.001878527,0.0001618936],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0000872106,0.00005125722,0.0004525551,0.06651884,0.0003195638,0.0002083234,0.0003541067,0.0007255946,0.001529466,0.006287145,0.03128953,0.8921764],"study_design_scores_gemma":[0.00001945209,0.0001453841,0.001354594,0.0294893,0.0006262243,0.001167028,0.0002687738,0.0004561404,0.001108269,0.006434415,0.9588453,0.00008512827],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.000175963,0.9949678,0.002473415,0.001231592,0.0002990515,0.00002495105,0.0001033602,0.00005168114,0.0006723574],"genre_scores_gemma":[0.0009395173,0.9947738,0.003020245,0.0005830022,0.0002709315,0.00003772999,0.0001577064,0.0000189943,0.0001981616],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.006503524,"threshold_uncertainty_score":0.03439432,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1359050961251432,"score_gpt":0.452918929998078,"score_spread":0.3170138338729349,"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."}}