{"id":"W2102011884","doi":"10.1002/nbm.1359","title":"Trading off SNR and resolution in MR images","year":2009,"lang":"en","type":"article","venue":"NMR in Biomedicine","topic":"Advanced MRI Techniques and Applications","field":"Medicine","cited_by":34,"is_retracted":false,"has_abstract":true,"ca_institutions":"Hospital for Sick Children; University of Toronto","funders":"","keywords":"Neuroanatomy; Consistency (knowledge bases); Magnetic resonance imaging; Image quality; Signal-to-noise ratio (imaging); Image resolution; Isotropy; Resolution (logic); Computer science; Artificial intelligence; Computer vision; Range (aeronautics); Nuclear magnetic resonance; Image (mathematics); Physics; Optics; Psychology; Medicine; Materials science; Neuroscience","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":[0.005964616,0.000709502,0.0006267632,0.0006908305,0.0003423613,0.001827265,0.0008379942,0.00122053,0.001855237],"category_scores_gemma":[0.04926884,0.0007458581,0.0004445721,0.000659279,0.001168796,0.002791447,0.001832896,0.0008723052,0.0005578814],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006410861,"about_ca_system_score_gemma":0.0002638635,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003074671,"about_ca_topic_score_gemma":0.0004711623,"domain_scores_codex":[0.9951572,0.002424081,0.0004640804,0.0007896209,0.0009013678,0.0002636673],"domain_scores_gemma":[0.9704388,0.02395353,0.001555045,0.001817964,0.001858655,0.0003760647],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.004385586,0.0001280626,0.008320756,0.0007674327,0.0002416432,0.0008664467,0.001019063,0.01423409,0.8765236,0.004773797,0.0004273894,0.0883121],"study_design_scores_gemma":[0.0004132427,0.004142104,0.04347556,0.0002636936,0.0005606124,0.007188441,0.0006165148,0.06779566,0.84807,0.02187131,0.005296829,0.0003060577],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"methods","genre_scores_codex":[0.7461897,0.003012042,0.2418542,0.0008393337,0.00009932513,0.0001427182,0.0001510615,0.0007314434,0.006980084],"genre_scores_gemma":[0.8530254,0.0008484425,0.1440341,0.0003148039,0.00006789362,0.00008543229,0.0001061494,0.0004153273,0.001102436],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.005964616,"threshold_uncertainty_score":0.03154433,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01832328429340963,"score_gpt":0.3415682798626244,"score_spread":0.3232449955692148,"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."}}