{"id":"W2070928042","doi":"10.1021/ja073238x","title":"Solid-State<sup>63</sup>Cu and<sup>65</sup>Cu NMR Spectroscopy of Inorganic and Organometallic Copper(I) Complexes","year":2007,"lang":"en","type":"article","venue":"Journal of the American Chemical Society","topic":"Advanced NMR Techniques and Applications","field":"Chemistry","cited_by":74,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Windsor","funders":"Cornell Center for Materials Research","keywords":"Chemistry; Copper; Electric field gradient; J-coupling; NMR spectra database; Crystallography; Nuclear magnetic resonance spectroscopy; Tensor (intrinsic definition); Anisotropy; Spectroscopy; Yield (engineering); Spectral line; Carbon-13 NMR satellite; Coupling constant; Solid-state nuclear magnetic resonance; Quadrupole; Nuclear magnetic resonance; Fluorine-19 NMR; Atomic physics; Stereochemistry; Geometry; 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":[0.000208962,0.0001989368,0.0001620673,0.0001431535,0.000260118,0.0002962499,0.0003262205,0.0002494867,0.0007711951],"category_scores_gemma":[0.0004110244,0.0001343138,0.00007925684,0.0002079929,0.0003474409,0.0001764161,0.0001675914,0.000266408,0.0001689545],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003371948,"about_ca_system_score_gemma":0.0001712538,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009636952,"about_ca_topic_score_gemma":0.001124687,"domain_scores_codex":[0.9998689,0.00002660962,0.000006895699,0.00002820396,0.00004920177,0.00002018942],"domain_scores_gemma":[0.9998521,0.00003906968,0.00002800758,0.00001803591,0.00004007641,0.00002277713],"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":[0.0001592279,0.00001207876,0.0004223726,0.00006994669,0.000008390333,0.0000854995,0.00009693612,0.0007291954,0.9957829,0.0003912558,0.0001867837,0.002055533],"study_design_scores_gemma":[0.0000123881,0.0001054779,0.001962334,0.000004529525,0.000007172756,0.0001149709,0.00006367416,0.003197821,0.9929848,0.00009870986,0.001436397,0.00001167183],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9952531,0.0003048443,0.002328105,0.0000407724,0.000007736234,0.000006725415,0.0001937396,0.00008643436,0.001778636],"genre_scores_gemma":[0.9957263,0.0001332762,0.003103018,0.0000146831,0.000003342606,0.000008219724,0.0002872891,0.00001654748,0.0007073609],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0009636952,"threshold_uncertainty_score":0.002579927,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009838969222339353,"score_gpt":0.2913318929886518,"score_spread":0.2814929237663124,"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."}}