{"id":"W2412923612","doi":"10.1039/c6cp02855a","title":"Natural abundance <sup>14</sup>N and <sup>15</sup>N solid-state NMR of pharmaceuticals and their polymorphs","year":2016,"lang":"en","type":"article","venue":"Physical Chemistry Chemical Physics","topic":"Advanced NMR Techniques and Applications","field":"Chemistry","cited_by":68,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Windsor","funders":"Natural Sciences and Engineering Research Council of Canada; U.S. Department of Energy","keywords":"Natural abundance; Chemistry; Abundance (ecology); NMR spectra database; Solid-state; Solid-state nuclear magnetic resonance; Nuclear magnetic resonance spectroscopy; Nuclear chemistry; Crystallography; Analytical Chemistry (journal); Spectral line; Physical chemistry; Stereochemistry; Physics; Nuclear magnetic resonance; Mass spectrometry; Organic chemistry; Chromatography","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":[0.00005962165,0.0006134417,0.0007540736,0.00001171373,0.0001194036,0.00003996224,0.000476287,0.0001936067,0.0000897663],"category_scores_gemma":[0.00009573349,0.0004600907,0.0002436593,0.0002093104,0.001098218,0.0002584427,0.0004264462,0.0006198206,0.00001102438],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009864506,"about_ca_system_score_gemma":0.00004890832,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000004856476,"about_ca_topic_score_gemma":4.17919e-8,"domain_scores_codex":[0.9975505,0.00001341604,0.0005015547,0.0009166896,0.0003296047,0.0006882084],"domain_scores_gemma":[0.9979923,0.0005211284,0.0002424598,0.0007224524,0.0001613669,0.0003602653],"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":[0.00008296405,0.000366371,0.00006082991,0.0003808852,0.00007654865,0.000002170282,0.0002206505,0.00006381455,0.9798374,0.000391845,0.0003846214,0.01813186],"study_design_scores_gemma":[0.000928713,0.00001213146,0.000004271491,0.0001757449,0.00005564115,0.00001588814,0.00005467153,0.01474721,0.9519033,0.02900323,0.002523801,0.0005754005],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9941489,0.0003960202,0.00185715,0.0005282511,0.000003489464,0.0001581623,0.0004646832,0.0002826805,0.002160667],"genre_scores_gemma":[0.9975303,0.0001987533,0.0006702699,0.000157689,0.0006327732,0.00009916888,0.00006556601,0.00008732898,0.0005581166],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02861139,"threshold_uncertainty_score":0.9997851,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01299780054367962,"score_gpt":0.2886063402809768,"score_spread":0.2756085397372972,"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."}}