{"id":"W2052213438","doi":"10.1016/j.jmr.2005.09.002","title":"Application of the chirp z-transform to MRI data","year":2005,"lang":"en","type":"article","venue":"Journal of Magnetic Resonance","topic":"Advanced NMR Techniques and Applications","field":"Chemistry","cited_by":20,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of New Brunswick","funders":"Deutsche Forschungsgemeinschaft","keywords":"k-space; Chirp; Fourier transform; Algorithm; Interpolation (computer graphics); Scaling; Physics; Optics; Computer science; Nuclear magnetic resonance; Artificial intelligence; Mathematics; Mathematical analysis; Image (mathematics); Geometry","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003583697,0.0004034046,0.0002402632,0.0009493017,0.0002524538,0.0007422542,0.0004117081,0.0004324408,0.004884124],"category_scores_gemma":[0.002429455,0.0002541617,0.0002230875,0.001185995,0.0003700031,0.0006297388,0.0007005726,0.0006687068,0.001636286],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001495575,"about_ca_system_score_gemma":0.000416668,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007123653,"about_ca_topic_score_gemma":0.0008734892,"domain_scores_codex":[0.9998399,0.00003583001,0.000009078471,0.00002463855,0.00007467715,0.00001579894],"domain_scores_gemma":[0.9995157,0.000228404,0.00003283359,0.0000888779,0.0001085306,0.0000256951],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0003907031,0.00007323957,0.0008125486,0.0003496113,0.00004126206,0.0006470818,0.0001756673,0.02016529,0.4612698,0.01299509,0.002439847,0.50064],"study_design_scores_gemma":[0.00007231039,0.0002049692,0.003641815,0.00005910888,0.00005286503,0.002392719,0.0001768385,0.6187037,0.3407247,0.01315026,0.02076538,0.00005538137],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05679148,0.0006686319,0.930221,0.000620981,0.0001269873,0.0001055795,0.0003941215,0.002242497,0.008828837],"genre_scores_gemma":[0.2839388,0.001727071,0.7096887,0.0001739493,0.00009643658,0.00007269481,0.0005736569,0.0004031261,0.003325487],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004884124,"threshold_uncertainty_score":0.016339,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01351387988187611,"score_gpt":0.2896926993864826,"score_spread":0.2761788195046064,"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."}}