{"id":"W2117844636","doi":"10.1002/nbm.3023","title":"High‐resolution MRI encoding using radiofrequency phase gradients","year":2013,"lang":"en","type":"article","venue":"NMR in Biomedicine","topic":"Advanced MRI Techniques and Applications","field":"Medicine","cited_by":23,"is_retracted":false,"has_abstract":true,"ca_institutions":"National Research Council Canada; Alberta Innovates","funders":"University of Calgary","keywords":"Nuclear magnetic resonance; Resolution (logic); Phase (matter); Encoding (memory); Materials science; Chemistry; Physics; Computer science; Artificial intelligence","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":[0.0001345477,0.0002602626,0.0001530967,0.0001577627,0.00007915902,0.0004175257,0.0002409179,0.0002089723,0.001457571],"category_scores_gemma":[0.000224603,0.0001808256,0.0001136263,0.0001595329,0.0001975868,0.0004651151,0.0002729813,0.0002874454,0.0006006535],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001115707,"about_ca_system_score_gemma":0.0001579469,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002137283,"about_ca_topic_score_gemma":0.0002756962,"domain_scores_codex":[0.9999391,0.000009858939,0.000003493516,0.00001319751,0.00001932172,0.00001503171],"domain_scores_gemma":[0.9998777,0.00003837756,0.00003339178,0.00001411703,0.00002509276,0.00001125247],"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":[0.00004568843,0.000007785584,0.00006166818,0.00002722629,0.000002254829,0.00004678943,0.00001676949,0.0004714202,0.9895524,0.0005343672,0.0001245205,0.009109144],"study_design_scores_gemma":[0.00001726464,0.0001543996,0.0005704581,0.000006482005,0.000008112147,0.0003670244,0.00001277291,0.007840426,0.9849163,0.0003731198,0.005721752,0.00001189442],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5919217,0.002786534,0.3872874,0.0005960557,0.0001533664,0.0001163711,0.0002062329,0.001490691,0.01544173],"genre_scores_gemma":[0.8021495,0.001146717,0.1898009,0.0001900699,0.0000570664,0.00006333154,0.000213344,0.0001439114,0.006235111],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001457571,"threshold_uncertainty_score":0.004876077,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03441546419039166,"score_gpt":0.3613693623931163,"score_spread":0.3269538982027247,"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."}}