{"id":"W4392764353","doi":"","title":"Exploration of TRASE MRI at Low Magnetic Field: Potential Performance and Limitations","year":2018,"lang":"en","type":"article","venue":"HAL (Le Centre pour la Communication Scientifique Directe)","topic":"Advanced MRI Techniques and Applications","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Winnipeg","funders":"","keywords":"Magnetic field; Computer science; Magnetic resonance imaging; Biomagnetism; Nuclear magnetic resonance; Physics; Medicine; Radiology","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":[0.003768952,0.0009092929,0.001070361,0.0009982527,0.0008131372,0.003006772,0.00136296,0.002692016,0.01430936],"category_scores_gemma":[0.008477055,0.0005851689,0.0003477071,0.0008726693,0.001427981,0.003719697,0.001504823,0.001562168,0.003429185],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005029164,"about_ca_system_score_gemma":0.0009408896,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00116173,"about_ca_topic_score_gemma":0.002217508,"domain_scores_codex":[0.9994636,0.0002218623,0.00002868031,0.00008409908,0.0001386254,0.00006314825],"domain_scores_gemma":[0.9928543,0.004452073,0.000354155,0.0006261622,0.001266219,0.0004472143],"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":[0.003674096,0.0002688445,0.005212468,0.004307526,0.0002198455,0.0008781778,0.0007254238,0.006835603,0.8315765,0.008047015,0.006605078,0.1316493],"study_design_scores_gemma":[0.0002508646,0.005834572,0.01181463,0.001445042,0.0005497403,0.009203535,0.001393388,0.06129879,0.7942408,0.01764063,0.09604044,0.0002875741],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3742651,0.05439555,0.4779279,0.01629582,0.001342906,0.0008522344,0.002230259,0.007392491,0.06529775],"genre_scores_gemma":[0.7085342,0.02276077,0.2415272,0.002728891,0.001002111,0.0004322918,0.00136308,0.001208804,0.02044274],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01430936,"threshold_uncertainty_score":0.04786962,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01896154602311166,"score_gpt":0.2529112578948877,"score_spread":0.233949711871776,"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."}}