{"id":"W2903014297","doi":"10.1002/cjce.23409","title":"Experimental Methods in Chemical Engineering: Nuclear Magnetic Resonance","year":2018,"lang":"en","type":"article","venue":"The Canadian Journal of Chemical Engineering","topic":"NMR spectroscopy and applications","field":"Physics and Astronomy","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"Nuclear magnetic resonance spectroscopy; Nuclear reaction analysis; Spectroscopy; Density functional theory; Materials science; NMR spectra database; Polymer; Nuclear magnetic resonance; Analytical Chemistry (journal); Chemistry; Chemical physics; Spectral line; Computational chemistry; Physics; Organic chemistry; Ion","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":true,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001453262,0.0001076394,0.0001453134,0.00006497139,0.00003637194,0.0000369653,0.0002952382,0.00003696225,0.000314155],"category_scores_gemma":[0.00002158857,0.00009329657,0.00006699306,0.0001807108,0.00006744815,0.00005544389,0.00001729824,0.0003414322,0.000009569386],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001117749,"about_ca_system_score_gemma":0.00009613878,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004374738,"about_ca_topic_score_gemma":0.000004209168,"domain_scores_codex":[0.9993312,0.000008078937,0.0002284742,0.00008673456,0.00007484973,0.0002706988],"domain_scores_gemma":[0.999506,0.00004633944,0.00004213906,0.0001361053,0.00003127026,0.0002381752],"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":[0.000005006355,0.00001367947,0.00009928861,0.000002287001,0.000008585725,0.000003026086,0.000321265,0.0001729297,0.982729,0.01497447,0.0002279385,0.001442518],"study_design_scores_gemma":[0.0002526407,0.00002868511,0.0001289549,0.00004539243,0.000008774174,0.00002022614,0.00002624107,0.008966666,0.974664,0.0003300778,0.01538882,0.0001394849],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.994875,0.000769221,0.002648102,0.0004458401,0.0001659157,0.0000813235,0.000005202919,0.0000118318,0.0009976365],"genre_scores_gemma":[0.981612,1.764907e-7,0.017743,0.00003217749,0.0005726657,0.000004711183,6.363917e-7,0.00002075452,0.00001385856],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01516088,"threshold_uncertainty_score":0.3804524,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007630375064827091,"score_gpt":0.285144944817755,"score_spread":0.2775145697529279,"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."}}