{"id":"W2055142458","doi":"10.1002/mrc.884","title":"Gradient‐selected versus phase‐cycled HMBC and HSQC: pros and cons","year":2001,"lang":"en","type":"article","venue":"Magnetic Resonance in Chemistry","topic":"Advanced MRI Techniques and Applications","field":"Medicine","cited_by":23,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"Dirección General de Asuntos del Personal Académico, Universidad Nacional Autónoma de México; Natural Sciences and Engineering Research Council of Canada","keywords":"Chemistry; Phase (matter); Heteronuclear single quantum coherence spectroscopy; Spectral line; Pulse sequence; Analytical Chemistry (journal); Nuclear magnetic resonance; Noise (video); Signal-to-noise ratio (imaging); Sequence (biology); Computational physics; Nuclear magnetic resonance spectroscopy; Physics; Chromatography; Optics; Stereochemistry","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":[],"consensus_categories":[],"category_scores_codex":[0.00004002049,0.0001221662,0.0001770976,0.00002003651,0.00004634501,0.00001201855,0.00004851404,0.00008647365,0.0001026323],"category_scores_gemma":[0.00008915341,0.0001205747,0.00001495222,0.0002428327,0.0001757087,0.00002690369,0.00003279569,0.0001722418,0.000001238453],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003379552,"about_ca_system_score_gemma":0.00003454882,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000009079397,"about_ca_topic_score_gemma":0.000004321928,"domain_scores_codex":[0.9992,0.000004611159,0.0001761363,0.0002961184,0.0001075388,0.0002155538],"domain_scores_gemma":[0.9995435,0.00005319218,0.00003671401,0.0002189806,0.00004391838,0.0001036696],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"not_applicable","study_design_scores_codex":[0.001405505,0.000697321,0.02801322,0.0002045981,0.000005897499,0.0001983782,0.0002179602,0.0000012623,0.5448721,0.0003419616,0.001496888,0.4225449],"study_design_scores_gemma":[0.03752335,0.00161761,0.08935105,0.0007160734,0.0001582135,0.001324443,0.0005341061,0.005817792,0.2627573,0.003450062,0.5956655,0.001084485],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.985827,0.005242152,0.0002273536,0.0008524819,0.00001052045,0.0003913643,0.000008402445,0.00008523405,0.0073555],"genre_scores_gemma":[0.9884463,0.002837633,0.00625219,0.00006713596,0.00005364009,0.0002102227,0.00001883106,0.00001755259,0.002096522],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5941686,"threshold_uncertainty_score":0.4916891,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01628564018869732,"score_gpt":0.3159978872417069,"score_spread":0.2997122470530095,"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."}}