{"id":"W2014591895","doi":"10.1016/j.jmr.2013.01.008","title":"Multi-Frame SPRITE: A method for resolution enhancement of multiple-point SPRITE data","year":2013,"lang":"en","type":"article","venue":"Journal of Magnetic Resonance","topic":"Advanced MRI Techniques and Applications","field":"Medicine","cited_by":12,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of New Brunswick","funders":"National Institute for Health and Care Research; Cancer Research UK","keywords":"Sprite (computer graphics); Image resolution; Computer science; Computer vision; Optics; Iterative reconstruction; Zoom; Artificial intelligence; Algorithm; Physics","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":[0.001916775,0.001372771,0.000701028,0.002627098,0.0007393626,0.001361595,0.001817863,0.001405251,0.008327505],"category_scores_gemma":[0.004717496,0.0007119625,0.0008323182,0.002059708,0.0005634328,0.001802444,0.001773971,0.001805432,0.002566915],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004060701,"about_ca_system_score_gemma":0.0009618418,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001704394,"about_ca_topic_score_gemma":0.004007236,"domain_scores_codex":[0.9993767,0.0001360598,0.00003629193,0.00007674889,0.0003097083,0.0000644057],"domain_scores_gemma":[0.9978994,0.0007201139,0.0001800369,0.0004271415,0.0006014528,0.0001718827],"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":[0.001282104,0.0002570862,0.0009796914,0.0005271515,0.000212891,0.0005259383,0.0004254879,0.02577896,0.2037423,0.01502238,0.01257062,0.7386754],"study_design_scores_gemma":[0.00009236435,0.0002236621,0.001609974,0.00005388047,0.00009367975,0.001152619,0.00013345,0.7634161,0.1885707,0.008335897,0.03619988,0.0001178244],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.005052927,0.000128708,0.9918258,0.00006174769,0.0000365419,0.0000493004,0.0001359355,0.00192899,0.0007799561],"genre_scores_gemma":[0.03281556,0.0002280047,0.9632661,0.00005339624,0.0000469978,0.00009129277,0.0004908729,0.0009584482,0.002049245],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.008327505,"threshold_uncertainty_score":0.02785832,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05444689523660481,"score_gpt":0.3753238014148637,"score_spread":0.3208769061782588,"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."}}