{"id":"W2173930931","doi":"10.1139/v11-049","title":"Structural analysis of lanthanum-containing battery materials using <sup>139</sup>La solid-state NMR","year":2011,"lang":"en","type":"article","venue":"Canadian Journal of Chemistry","topic":"Advanced NMR Techniques and Applications","field":"Chemistry","cited_by":19,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary; Steacie Institute for Molecular Sciences; University of Ottawa; Brockhouse Institute for Materials Research","funders":"McMaster University; Arnold and Mabel Beckman Initiative for Macular Research","keywords":"CASTEP; Chemistry; Lanthanum; Solid-state nuclear magnetic resonance; Heteronuclear molecule; NMR spectra database; Lithium (medication); Coupling constant; Physical chemistry; Analytical Chemistry (journal); Crystallography; Spectral line; Nuclear magnetic resonance spectroscopy; Density functional theory; Inorganic chemistry; Computational chemistry; Nuclear magnetic resonance; Stereochemistry","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.000156732,0.0001864667,0.0004912509,0.0001448679,0.00009360071,0.0000366188,0.0003926041,0.0001441579,0.003860589],"category_scores_gemma":[0.00005307947,0.0001874396,0.00020633,0.0002496064,0.0001505434,0.0001312542,0.00002369578,0.0002453026,7.476242e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001588618,"about_ca_system_score_gemma":0.0004309299,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001205981,"about_ca_topic_score_gemma":0.0001741139,"domain_scores_codex":[0.9986968,0.00001316333,0.0006583082,0.0001690396,0.0001379115,0.0003247519],"domain_scores_gemma":[0.99843,0.00004226098,0.0006204715,0.0003146742,0.0001922606,0.0004003812],"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":[0.00004645735,0.00001337657,0.008524308,0.0001536252,0.0007760314,0.0001902091,0.001231842,0.005536047,0.9824788,0.00002720259,0.0001391241,0.000883005],"study_design_scores_gemma":[0.0002253553,0.000009286668,0.0004610717,0.0001458008,0.000431906,0.0002441634,0.0005936412,0.001349553,0.9951453,0.0006939824,0.0004557684,0.0002441412],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9955317,0.0001118957,0.0006478694,0.00001881219,0.00000891131,0.00002289557,0.0002998294,0.00001311148,0.003344993],"genre_scores_gemma":[0.9971905,0.00001275016,0.002424131,0.00003476065,0.0001151437,0.000002462275,0.00003235364,0.0000269092,0.0001609698],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01266656,"threshold_uncertainty_score":0.99705,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02737850786827363,"score_gpt":0.2733462387097088,"score_spread":0.2459677308414352,"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."}}