{"id":"W2127471560","doi":"10.1002/mrc.2522","title":"The signal/noise of an HMBC spectrum can depend dramatically upon the choice of acquisition and processing parameters","year":2009,"lang":"en","type":"article","venue":"Magnetic Resonance in Chemistry","topic":"Spectroscopy and Quantum Chemical Studies","field":"Physics and Astronomy","cited_by":18,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Weighting; Chemistry; Sine; Gaussian; Sensitivity (control systems); Noise (video); SIGNAL (programming language); Exponential function; Spectral line; Range (aeronautics); Biological system; Signal processing; Molecule; Analytical Chemistry (journal); Spectrum (functional analysis); Statistical physics; Algorithm; Computational chemistry; Mathematical analysis; Physics; Quantum mechanics; Chromatography; Acoustics; Electronic engineering; Digital signal processing; Organic chemistry; Artificial intelligence; Mathematics","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001205595,0.0001154666,0.0001706335,0.000005833155,0.00009739709,0.00003180051,0.0001823952,0.0000328914,0.00003900765],"category_scores_gemma":[0.00001873803,0.00007378293,0.00003462647,0.0001167308,0.0001706394,0.00003669697,0.00002867855,0.0001466054,1.482967e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001378663,"about_ca_system_score_gemma":0.00002504295,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000139871,"about_ca_topic_score_gemma":0.00001626563,"domain_scores_codex":[0.9991873,0.00001968135,0.0002660019,0.0001718713,0.0001505794,0.0002045948],"domain_scores_gemma":[0.9994621,0.000180295,0.0001177195,0.00018165,0.00002429796,0.00003389084],"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":[0.0002308624,0.0004006914,0.02495204,0.0001916488,0.00001769973,0.00000300099,0.000976341,0.00009639403,0.6547284,0.001139655,0.00004769903,0.3172156],"study_design_scores_gemma":[0.0005245802,0.0001547618,0.05731566,0.0001846332,0.00002667948,0.000001609263,0.0006438787,0.00370902,0.9124383,0.0246275,0.0002065556,0.0001668293],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9950829,0.002488701,0.00002680235,0.000693311,0.000006488457,0.0001029717,0.000008606918,0.000005234431,0.001584959],"genre_scores_gemma":[0.9996033,0.00003685969,0.0002288345,0.00002461898,0.00005105967,0.00001350245,0.000003371727,0.00000517843,0.00003322096],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3170488,"threshold_uncertainty_score":0.3008781,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007082680549057645,"score_gpt":0.2471833973396041,"score_spread":0.2401007167905465,"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."}}