{"id":"W1975499624","doi":"10.1016/j.jmr.2012.04.004","title":"Designing optimal universal pulses using second-order, large-scale, non-linear optimization","year":2012,"lang":"en","type":"article","venue":"Journal of Magnetic Resonance","topic":"Electron Spin Resonance Studies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":23,"is_retracted":false,"has_abstract":false,"ca_institutions":"Western University; McMaster University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Pulse (music); Relaxation (psychology); Pulse sequence; Order (exchange); Time derivative; Computer science; Series (stratigraphy); Constant (computer programming); Scale (ratio); Derivative (finance); Algorithm; Physics; Mathematical optimization; Applied mathematics; Nuclear magnetic resonance; Mathematics; Mathematical analysis; Quantum mechanics; Telecommunications","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006281362,0.0007041036,0.0006974181,0.0003483335,0.0003563287,0.000598921,0.0006251708,0.0009057783,0.001969877],"category_scores_gemma":[0.002287739,0.0005366476,0.0003457063,0.0003854682,0.0007883023,0.0008661245,0.0009464456,0.0007578738,0.0003468323],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006910356,"about_ca_system_score_gemma":0.0009337745,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008745731,"about_ca_topic_score_gemma":0.001749888,"domain_scores_codex":[0.999793,0.000053441,0.000009400549,0.00004358858,0.00006279317,0.00003774069],"domain_scores_gemma":[0.9993204,0.000492213,0.00005758294,0.00003190804,0.00006864226,0.00002938257],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001508396,0.000104599,0.0003726214,0.0002077214,0.00004657674,0.00006362632,0.0001716584,0.8762156,0.03403563,0.02729848,0.000788923,0.06054373],"study_design_scores_gemma":[0.00001105564,0.00003798352,0.00005747858,0.000005929305,0.000004351928,0.00001033568,0.00001289678,0.9932603,0.002782692,0.003417886,0.0003927377,0.000006269681],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03657977,0.0002227099,0.9583367,0.0001895255,0.00003591579,0.00006073048,0.00001497183,0.0001978184,0.004361793],"genre_scores_gemma":[0.6632264,0.0002694844,0.331662,0.0001440745,0.00003317648,0.000303658,0.00004322986,0.000125614,0.004192417],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001969877,"threshold_uncertainty_score":0.00658989,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01113173982650152,"score_gpt":0.2604624751486001,"score_spread":0.2493307353220986,"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."}}