{"id":"W1976101179","doi":"10.1002/mrm.23043","title":"Three‐dimensional MRI with independent slab excitation and encoding","year":2011,"lang":"en","type":"article","venue":"Magnetic Resonance in Medicine","topic":"Advanced MRI Techniques and Applications","field":"Medicine","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"Canadian Institutes of Health Research","keywords":"Excitation; Slab; Nuclear magnetic resonance; Encoding (memory); Magnetic resonance imaging; Physics; Materials science; Computer science; Medicine; Artificial intelligence; Radiology; Geophysics; Quantum mechanics","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0006616984,0.0006612625,0.0003829576,0.0005937896,0.0003280177,0.001073909,0.0009990851,0.0006659121,0.002100453],"category_scores_gemma":[0.001551847,0.0007348544,0.0004005964,0.0006930917,0.0007193242,0.0008207768,0.001008975,0.001210882,0.0009529908],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002526693,"about_ca_system_score_gemma":0.0006806951,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005693248,"about_ca_topic_score_gemma":0.001186877,"domain_scores_codex":[0.9997583,0.00006586857,0.00002249691,0.00004365712,0.00008861523,0.00002100233],"domain_scores_gemma":[0.9993089,0.0001978011,0.0001171416,0.000225886,0.0001045484,0.00004571967],"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":[0.0003782183,0.0001240119,0.0009557948,0.0004797433,0.0001074167,0.0004100775,0.0002220699,0.009092263,0.7975717,0.02073766,0.003429758,0.1664912],"study_design_scores_gemma":[0.0001708515,0.0006778659,0.004607896,0.0001925429,0.0002221152,0.004995146,0.0001074536,0.0871433,0.8021561,0.02095064,0.07852226,0.0002537497],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01892473,0.000493018,0.9759326,0.000231855,0.00006427969,0.00007576152,0.000131787,0.0008465059,0.00329944],"genre_scores_gemma":[0.08632067,0.0008449668,0.9104007,0.0001662495,0.00004075207,0.0001708249,0.0001928408,0.0002640192,0.00159896],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.002100453,"threshold_uncertainty_score":0.007026672,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03290955310373084,"score_gpt":0.2929438164850445,"score_spread":0.2600342633813136,"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."}}