{"id":"W2043611839","doi":"10.1063/1.1287327","title":"Dual Lanczos simulation of dynamic nuclear magnetic resonance spectra for systems with many spins or exchange sites","year":2000,"lang":"en","type":"article","venue":"The Journal of Chemical Physics","topic":"Advanced NMR Techniques and Applications","field":"Chemistry","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Lanczos resampling; Tridiagonal matrix; Spins; Hermitian matrix; Lanczos algorithm; Magnetization; Physics; Spectral line; NMR spectra database; Applied mathematics; Nuclear magnetic resonance; Chemistry; Mathematics; Condensed matter physics; Quantum mechanics; Eigenvalues and eigenvectors; Magnetic field","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":[0.000274028,0.0002719227,0.0004792406,0.000184115,0.0003499208,0.0003977512,0.0006789251,0.0006606074,0.001707711],"category_scores_gemma":[0.0009922703,0.0002021358,0.0002196091,0.0002535064,0.0004493,0.0003211309,0.0004090222,0.0003939179,0.000187216],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003668839,"about_ca_system_score_gemma":0.0008280275,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005383528,"about_ca_topic_score_gemma":0.005180649,"domain_scores_codex":[0.9999005,0.00003410696,0.000004426758,0.000008518879,0.00003635267,0.00001612162],"domain_scores_gemma":[0.9997303,0.0001243025,0.00002914476,0.00002670055,0.0000486579,0.00004086273],"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":[0.000204598,0.00007072594,0.0006500302,0.00007030369,0.00001615622,0.0001475264,0.0001049431,0.9493285,0.02213153,0.01357677,0.0009515709,0.01274729],"study_design_scores_gemma":[0.00001171517,0.000007400943,0.00002033621,5.833936e-7,5.948087e-7,0.00000336457,0.000002735136,0.9987637,0.0006496426,0.0003967697,0.0001418946,0.000001364429],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5404217,0.0003062012,0.4419461,0.0006351834,0.0001055661,0.0001310017,0.0002333564,0.001598743,0.01462218],"genre_scores_gemma":[0.8520492,0.0001297258,0.1422877,0.00007028523,0.0000233774,0.0001187483,0.0001818526,0.00009549597,0.005043644],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.005383528,"threshold_uncertainty_score":0.0107044,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01514818166996749,"score_gpt":0.2756220003519434,"score_spread":0.2604738186819759,"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."}}