{"id":"W2076178105","doi":"10.1016/j.jmr.2007.07.009","title":"Determination of NMR interaction parameters from double rotation NMR","year":2007,"lang":"en","type":"article","venue":"Journal of Magnetic Resonance","topic":"Advanced NMR Techniques and Applications","field":"Chemistry","cited_by":31,"is_retracted":false,"has_abstract":false,"ca_institutions":"Queen's University","funders":"Engineering and Physical Sciences Research Council","keywords":"Electric field gradient; Heteronuclear molecule; Chemistry; J-coupling; Anisotropy; Principal axis theorem; Nuclear magnetic resonance; Molecular physics; Carbon-13 NMR satellite; Dipole; Nuclear magnetic resonance spectroscopy; Coupling (piping); Orientation (vector space); Rotation (mathematics); Chemical shift; Fluorine-19 NMR; Atomic physics; Physics; Materials science; Quadrupole; Stereochemistry; Optics; Physical chemistry; 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":[0.0013373,0.001334353,0.0008901079,0.001665998,0.0008526886,0.0008521926,0.001217674,0.00106564,0.003217516],"category_scores_gemma":[0.004733911,0.0006551603,0.0005066946,0.001644455,0.0004551688,0.001817384,0.0006601655,0.002109825,0.001682283],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006100892,"about_ca_system_score_gemma":0.001030834,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001278846,"about_ca_topic_score_gemma":0.002205038,"domain_scores_codex":[0.9992449,0.0001606138,0.00004476372,0.0001653095,0.0002885477,0.00009592557],"domain_scores_gemma":[0.9978262,0.00091673,0.0002531362,0.0003399405,0.0005341551,0.000129767],"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":[0.0005966907,0.0001497557,0.001555603,0.0002996197,0.00006731696,0.0002162499,0.000172836,0.007363469,0.9426652,0.002761912,0.001485221,0.04266605],"study_design_scores_gemma":[0.00006333632,0.0003251839,0.006000774,0.00003184914,0.0001336474,0.0004787107,0.0001174812,0.1041354,0.8763977,0.002969358,0.009195695,0.0001509746],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.408159,0.002371004,0.5740947,0.0002390099,0.0001273451,0.0002292737,0.001897744,0.00320248,0.009679413],"genre_scores_gemma":[0.8098591,0.001509636,0.1820887,0.0001304673,0.0000397167,0.0003177799,0.003164603,0.001058502,0.001831533],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003217516,"threshold_uncertainty_score":0.01076365,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01876516473835357,"score_gpt":0.3058637296132324,"score_spread":0.2870985648748789,"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."}}