{"id":"W2057158361","doi":"10.1039/c5ce00060b","title":"Ultra-wideline<sup>14</sup>N solid-state NMR as a method for differentiating polymorphs: glycine as a case study","year":2015,"lang":"en","type":"article","venue":"CrystEngComm","topic":"Advanced NMR Techniques and Applications","field":"Chemistry","cited_by":35,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Windsor","funders":"Ontario Ministry of Research and Innovation; Natural Sciences and Engineering Research Council of Canada; University of Windsor","keywords":"Solid-state; Crystallography; Glycine; Chemistry; Solid-state nuclear magnetic resonance; Materials science; Physical chemistry; Computational chemistry; Nuclear magnetic resonance; Physics; Amino acid","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0004484431,0.0004140285,0.000552305,0.00008407937,0.0003408715,0.00009811304,0.0004212563,0.0001496627,0.0001469008],"category_scores_gemma":[0.0004920617,0.0003793983,0.000200021,0.0002288425,0.00004532771,0.0001499416,0.0001432094,0.0003780542,0.00003305132],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001383746,"about_ca_system_score_gemma":0.00009828633,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007405387,"about_ca_topic_score_gemma":0.00006530984,"domain_scores_codex":[0.9976819,0.00007376509,0.0007446444,0.0006512469,0.0002974269,0.0005510345],"domain_scores_gemma":[0.9974914,0.0004527128,0.0002970681,0.001010831,0.0003733648,0.0003746525],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.002559778,0.01481023,0.008815858,0.002578846,0.002880939,0.007665979,0.09113665,0.02824729,0.4266866,0.005353857,0.05212693,0.357137],"study_design_scores_gemma":[0.02676032,0.002862836,0.00002316205,0.0007405905,0.001750054,0.01511469,0.1894875,0.2820762,0.2415263,0.04811873,0.1865666,0.004973075],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7300632,0.0002940846,0.2630394,0.000565006,0.00002883245,0.001332616,0.0001884402,0.0008220911,0.003666384],"genre_scores_gemma":[0.9456987,0.0000085204,0.04834533,0.0003510487,0.0002994,0.001278279,0.00008529441,0.000115601,0.003817788],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3521639,"threshold_uncertainty_score":0.9998658,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04752517783634397,"score_gpt":0.3819727826485088,"score_spread":0.3344476048121648,"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."}}