{"id":"W3009866784","doi":"10.1002/mrm.28233","title":"Simple compensation method for improved half‐pulse excitation profile with rephasing gradient","year":2020,"lang":"en","type":"article","venue":"Magnetic Resonance in Medicine","topic":"Advanced MRI Techniques and Applications","field":"Medicine","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"Austrian Science Fund","keywords":"Calibration; Imaging phantom; Pulse (music); Image quality; Excitation; SIGNAL (programming language); Phase (matter); Optics; Distortion (music); Computer science; Mathematics; Physics; Image (mathematics); Artificial intelligence; Detector; Statistics","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":[0.0006537872,0.0009045282,0.0003592988,0.0003750272,0.000188413,0.0003811233,0.0007901991,0.0006243777,0.001786689],"category_scores_gemma":[0.00133224,0.0004172945,0.0002644841,0.0003907625,0.0002451095,0.0006068086,0.000427324,0.0006050394,0.0007764902],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001942965,"about_ca_system_score_gemma":0.0004208662,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003366073,"about_ca_topic_score_gemma":0.0008737743,"domain_scores_codex":[0.9996907,0.00005983964,0.00002013593,0.00007129063,0.0001367025,0.00002140212],"domain_scores_gemma":[0.9994814,0.000116155,0.0001066583,0.0001097294,0.0001625012,0.00002364342],"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":[0.000194543,0.00004491182,0.0006594654,0.0003493704,0.00003434054,0.00009983174,0.00007196531,0.002433641,0.9123265,0.0008382215,0.0005692596,0.0823778],"study_design_scores_gemma":[0.00004743686,0.0003268581,0.004406876,0.00003750561,0.0000986247,0.001753355,0.00002552719,0.07187536,0.9121887,0.0002856014,0.008872521,0.00008163325],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04406196,0.0008347412,0.9532304,0.000120027,0.00008644557,0.000082486,0.00005942762,0.0009477164,0.0005768196],"genre_scores_gemma":[0.1720266,0.0006092407,0.8254479,0.0001066153,0.00003631334,0.0000914192,0.0001579055,0.00022432,0.001299657],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001786689,"threshold_uncertainty_score":0.005977035,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03707257387352608,"score_gpt":0.3579141122246287,"score_spread":0.3208415383511026,"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."}}