{"id":"W2560814703","doi":"10.1016/j.ssnmr.2016.12.006","title":"Optimisation of excitation schemes for 14N overtone MAS NMR using numerically exact simulations","year":2016,"lang":"en","type":"article","venue":"Solid State Nuclear Magnetic Resonance","topic":"Advanced NMR Techniques and Applications","field":"Chemistry","cited_by":9,"is_retracted":false,"has_abstract":false,"ca_institutions":"National Research Council Canada","funders":"","keywords":"Overtone; Excitation; Pulse (music); Resonance (particle physics); Sensitivity (control systems); Nuclear magnetic resonance; Sweep frequency response analysis; Magnetic field; Pulse sequence; Field (mathematics); Computational physics; Materials science; Atomic physics; Chemistry; Acoustics; Physics; Optics; Electronic engineering; Mathematics; Engineering; Spectral line; Quantum mechanics","routes":{"ca_aff":true,"ca_fund":false,"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":[],"consensus_categories":[],"category_scores_codex":[0.00005467177,0.0001468211,0.0001887283,0.00004249658,0.000132072,0.00001641082,0.0001568936,0.00007303462,0.0004984947],"category_scores_gemma":[0.0001175619,0.000128136,0.00007410483,0.0001389381,0.000129501,0.0001679571,0.00003752172,0.0000586159,0.000008283423],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007634251,"about_ca_system_score_gemma":0.00003236763,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001095029,"about_ca_topic_score_gemma":0.0000016241,"domain_scores_codex":[0.9988993,0.00001090202,0.0003784635,0.0002968594,0.0001731533,0.0002413173],"domain_scores_gemma":[0.9990192,0.0001698244,0.0002170253,0.0003510281,0.000184431,0.00005846897],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0001039098,0.00007868182,0.0001001747,0.00006624649,0.000006778146,4.550466e-7,0.0001458018,0.002660479,0.8621057,0.003282416,0.0001831518,0.1312662],"study_design_scores_gemma":[0.003210514,0.0004127106,0.00342093,0.000641232,0.00008832689,0.000009524896,0.0001973243,0.2304912,0.1657053,0.05910047,0.5356895,0.001032981],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8644611,0.0003425954,0.1329805,0.0003351161,0.00001157084,0.0003783725,0.0003514707,0.0001690036,0.0009702807],"genre_scores_gemma":[0.8865431,0.0002885163,0.1116528,0.00004102575,0.00004222487,0.00004006564,0.000016682,0.00005273032,0.001322881],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6964004,"threshold_uncertainty_score":0.5458168,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02194441312149101,"score_gpt":0.3056771221745116,"score_spread":0.2837327090530206,"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."}}