{"id":"W2108530855","doi":"10.1139/v07-106","title":"Characterization of metallurgical-grade aluminas and their precursors by <sup>27</sup>Al NMR and XRD","year":2007,"lang":"en","type":"article","venue":"Canadian Journal of Chemistry","topic":"Advanced NMR Techniques and Applications","field":"Chemistry","cited_by":15,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Engineering and Physical Sciences Research Council; MacDiarmid Institute for Advanced Materials and Nanotechnology","keywords":"Magic angle spinning; Chemistry; Characterization (materials science); Solid-state nuclear magnetic resonance; XANES; Extended X-ray absorption fine structure; Nuclear magnetic resonance spectroscopy; Crystallography; Metallurgy; Materials science; Nuclear magnetic resonance; Spectroscopy; Nanotechnology; Stereochemistry; Absorption spectroscopy","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000143399,0.0001423895,0.0002266331,0.00003607726,0.00006636242,0.00002305701,0.0001539348,0.0001380436,0.0002189393],"category_scores_gemma":[0.00003930042,0.0001313664,0.00006942105,0.00007944958,0.0001958327,0.0001022554,0.00001388377,0.0002447294,2.56823e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000594456,"about_ca_system_score_gemma":0.000120488,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00007062779,"about_ca_topic_score_gemma":0.00003583483,"domain_scores_codex":[0.9991829,0.00000417251,0.0003628148,0.0001428166,0.00008932476,0.0002179357],"domain_scores_gemma":[0.9989271,0.00004317037,0.0002931055,0.0001506934,0.00009389631,0.0004920677],"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":[0.00001175658,0.00001922867,0.0005188002,0.00008177318,0.00003845703,0.000009920021,0.0002333137,0.000007380418,0.9956703,0.00003043515,0.0004625504,0.002916122],"study_design_scores_gemma":[0.0002717455,0.00001458995,0.0001714968,0.00009087672,0.0000266658,0.000280944,0.0004097498,0.0001442149,0.8892838,0.0002366894,0.1089143,0.0001548365],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9947458,0.0007221845,0.001705233,0.0004439746,0.000004194328,0.00003889792,0.0001259052,0.000009469868,0.002204345],"genre_scores_gemma":[0.9985297,0.0002077463,0.000457248,0.00005974701,0.00008179122,0.000002356531,0.00004692659,0.00001848499,0.0005959438],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1084518,"threshold_uncertainty_score":0.5356968,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007737180153083965,"score_gpt":0.2281102728092798,"score_spread":0.2203730926561958,"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."}}