{"id":"W2057758733","doi":"10.1016/j.mri.2007.02.020","title":"Phase-encoding strategies for optimal spatial resolution and T1 accuracy in 3D Look–Locker imaging","year":2007,"lang":"en","type":"article","venue":"Magnetic Resonance Imaging","topic":"Advanced MRI Techniques and Applications","field":"Medicine","cited_by":5,"is_retracted":false,"has_abstract":false,"ca_institutions":"Robarts Clinical Trials; Carleton University","funders":"","keywords":"Encoding (memory); Resolution (logic); Computer science; Phase (matter); Image resolution; Artificial intelligence; Computer vision; 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":[0.0007641762,0.0005718188,0.0003196046,0.0005847948,0.000334592,0.001019258,0.0006116469,0.0007915937,0.002317817],"category_scores_gemma":[0.003514802,0.0005485851,0.0002112572,0.0006371138,0.000419841,0.001363956,0.0007163634,0.0008180863,0.0006436154],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002039887,"about_ca_system_score_gemma":0.0004872481,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002842758,"about_ca_topic_score_gemma":0.000734527,"domain_scores_codex":[0.999799,0.00007848797,0.00001883521,0.00002443327,0.00005981298,0.00001948129],"domain_scores_gemma":[0.9989334,0.0006294292,0.0001215885,0.0001117528,0.0001606751,0.00004315264],"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":[0.000716011,0.0001515336,0.001249445,0.0004884769,0.00006366709,0.0002442097,0.0004294376,0.02543368,0.5225918,0.02725775,0.002574189,0.4187998],"study_design_scores_gemma":[0.0002077767,0.0003812357,0.001915677,0.00009457413,0.0001605641,0.002539786,0.0001747786,0.3956398,0.5617148,0.02540738,0.01163109,0.0001324917],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.03523809,0.0006357168,0.9620342,0.0002685661,0.00003357679,0.00003636311,0.00004900158,0.0004842604,0.001220247],"genre_scores_gemma":[0.1532021,0.0005388328,0.8449425,0.0001353637,0.00003212868,0.00006053285,0.00007105783,0.0003069253,0.0007105854],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.002317817,"threshold_uncertainty_score":0.007753849,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01794325472202754,"score_gpt":0.3466992993413091,"score_spread":0.3287560446192815,"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."}}