{"id":"W1984493340","doi":"10.1016/j.jmr.2005.07.006","title":"Acquisition time reduction in magnetic resonance spectroscopic imaging using discrete wavelet encoding","year":2005,"lang":"en","type":"article","venue":"Journal of Magnetic Resonance","topic":"Image and Signal Denoising Methods","field":"Computer Science","cited_by":14,"is_retracted":false,"has_abstract":false,"ca_institutions":"National Research Council Institute for Biodiagnostics","funders":"","keywords":"Wavelet; Discrete wavelet transform; Haar wavelet; Magnetic resonance spectroscopic imaging; Encoding (memory); Wavelet transform; Computer science; Nuclear magnetic resonance; Artificial intelligence; Physics; Mathematics; Magnetic resonance imaging","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":[0.001548007,0.0002296664,0.0003926634,0.0004058555,0.0001332682,0.0002548604,0.0007466017,0.00006755676,0.0001662033],"category_scores_gemma":[0.000120026,0.0002186112,0.0001263795,0.0007062971,0.0000946489,0.001410845,0.0001236791,0.0004375823,0.00003249541],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002967891,"about_ca_system_score_gemma":0.0001509949,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001816069,"about_ca_topic_score_gemma":0.000001309198,"domain_scores_codex":[0.9972588,0.0003728398,0.0008683503,0.0003798628,0.0006137065,0.0005063778],"domain_scores_gemma":[0.9987854,0.000118404,0.0003934932,0.0004066023,0.000183689,0.0001124402],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001231126,0.00008313922,0.0006241396,0.00002077513,0.000002117282,0.0003346471,0.0008111002,0.0004547472,0.22845,0.0005182024,0.0007156821,0.7678623],"study_design_scores_gemma":[0.007637449,0.001722828,0.1243851,0.002906977,0.00007798788,0.006611941,0.0001336074,0.7164474,0.07636558,0.01282017,0.04943458,0.001456434],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"methods","genre_scores_codex":[0.5777236,0.15164,0.2613065,0.004231257,0.001298686,0.000409178,0.000002903433,0.00008010064,0.003307832],"genre_scores_gemma":[0.4199093,0.0008458392,0.5762016,0.000367289,0.001044706,0.000003395124,5.014134e-7,0.00003069261,0.001596711],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7664059,"threshold_uncertainty_score":0.8914705,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01242146753755141,"score_gpt":0.2725625566735541,"score_spread":0.2601410891360026,"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."}}