{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001773402,0.00008076785,0.000186491,0.0002661967,0.00002101802,0.000002711225,0.00003534428,0.00006321642,0.00002002329],"category_scores_gemma":[0.00004071753,0.00006724976,0.00001331799,0.0004408796,0.00008027424,0.00004890604,0.000008993216,0.000151163,0.000001757471],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007036779,"about_ca_system_score_gemma":0.00001346307,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003325662,"about_ca_topic_score_gemma":0.000008760442,"domain_scores_codex":[0.9993317,0.000008274752,0.0002147441,0.0001815164,0.0001011061,0.0001626465],"domain_scores_gemma":[0.9997132,0.00002530125,0.00003378151,0.0001458278,0.0000161493,0.00006571924],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.0001985201,0.0005265479,0.01830856,0.00007114495,0.000003885001,0.00016308,0.0006002313,0.00001131627,0.449906,0.004386807,0.01028286,0.515541],"study_design_scores_gemma":[0.006869287,0.001635955,0.859243,0.001496713,0.00004859186,0.0003004568,0.0005952418,0.007505628,0.01736679,0.01889167,0.08566639,0.0003802608],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8932952,0.005722355,0.02286846,0.06012311,0.00005485591,0.00132133,0.00001118658,0.0002679346,0.01633551],"genre_scores_gemma":[0.9819474,0.001052814,0.01579777,0.000835366,0.00009941906,0.00001924836,0.00002233749,0.000006841452,0.0002188325],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8409345,"threshold_uncertainty_score":0.2742366,"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."}}