{"id":"W2808036167","doi":"10.1016/j.neuroimage.2018.06.036","title":"Whole head quantitative susceptibility mapping using a least-norm direct dipole inversion method","year":2018,"lang":"en","type":"article","venue":"NeuroImage","topic":"Advanced MRI Techniques and Applications","field":"Medicine","cited_by":46,"is_retracted":false,"has_abstract":false,"ca_institutions":"McGill University; University of Calgary","funders":"University of Melbourne; Canadian Institutes of Health Research; Alberta Innovates; Alberta Innovates - Health Solutions","keywords":"Quantitative susceptibility mapping; Imaging phantom; Tikhonov regularization; Dipole; Computer science; Algorithm; Artificial intelligence; Inverse problem; Mathematics; Nuclear magnetic resonance; Physics; Mathematical analysis; Optics; Magnetic resonance imaging; Radiology; Medicine","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00022153,0.0001450863,0.0002441587,0.00009052981,0.000213347,0.00001693051,0.00008916418,0.00005672263,0.00007595442],"category_scores_gemma":[0.0001657994,0.0001341024,0.00009010886,0.0003702967,0.0001911389,0.0001474409,0.00009942288,0.0002005995,0.0001430891],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008183183,"about_ca_system_score_gemma":0.00004729056,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001172797,"about_ca_topic_score_gemma":0.00002497898,"domain_scores_codex":[0.9988819,0.00007112787,0.0002098601,0.0004295446,0.0001617803,0.0002457737],"domain_scores_gemma":[0.9990705,0.00009358465,0.00009204487,0.0004695637,0.0001592671,0.0001149998],"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":[0.00007414083,0.0001288145,0.00151301,0.00003474965,0.00000574161,0.00001245955,0.0002625257,0.00001138287,0.9902533,0.0004115274,0.001419536,0.005872792],"study_design_scores_gemma":[0.002621226,0.002516843,0.0521108,0.00054145,0.000251221,0.0001738332,0.001622658,0.1394092,0.4424931,0.004911875,0.3524026,0.0009451606],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.3610286,0.00005318522,0.625036,0.00115328,0.00007237709,0.0006150163,0.0000283621,0.0003285129,0.01168472],"genre_scores_gemma":[0.4824677,0.00001136261,0.5156348,0.0008031902,0.0001315796,0.00001594512,0.00001751483,0.00003151727,0.0008863682],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.5477602,"threshold_uncertainty_score":0.5468537,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1170199416209945,"score_gpt":0.4276038997754282,"score_spread":0.3105839581544337,"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."}}