{"id":"W2741826558","doi":"10.1002/mrm.26830","title":"Quantitative susceptibility mapping: Report from the 2016 reconstruction challenge","year":2017,"lang":"en","type":"article","venue":"Magnetic Resonance in Medicine","topic":"Advanced MRI Techniques and Applications","field":"Medicine","cited_by":197,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia; University of British Columbia Hospital","funders":"National Center for Advancing Translational Sciences; National Institute of Biomedical Imaging and Bioengineering; Southern Medical University; Xiamen University; Universitätsklinikum Jena; Technische Universität München; Austrian Science Fund; University College London; Deutsches Zentrum für Neurodegenerative Erkrankungen; National Institutes of Health; Wayne State University","keywords":"Quantitative susceptibility mapping; Artificial intelligence; Mean squared error; Orientation (vector space); Computer science; Pattern recognition (psychology); Image quality; Mathematics; Smoothing; Algorithm; Computer vision; Magnetic resonance imaging; Statistics; Image (mathematics); Medicine","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":[0.03016932,0.002487459,0.001533868,0.001840702,0.001130031,0.002794104,0.002555757,0.003900445,0.002654466],"category_scores_gemma":[0.09314976,0.0006681701,0.001667299,0.001145978,0.001693768,0.001895009,0.004178909,0.002215351,0.003264152],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001375173,"about_ca_system_score_gemma":0.003481917,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003574875,"about_ca_topic_score_gemma":0.002951522,"domain_scores_codex":[0.9833609,0.005451177,0.001354396,0.001836397,0.007355808,0.0006411899],"domain_scores_gemma":[0.9217579,0.03285508,0.003066687,0.01247829,0.02600596,0.00383608],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.004438113,0.002258547,0.02665468,0.004572405,0.001506398,0.004509496,0.001983462,0.0446559,0.04348138,0.005367831,0.386458,0.4741138],"study_design_scores_gemma":[0.001753947,0.006226413,0.05404568,0.00210811,0.001009812,0.03002004,0.002023191,0.2386154,0.2131434,0.02227269,0.4277059,0.001075397],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.4568301,0.02602461,0.4034111,0.03345459,0.01008537,0.003337193,0.0262433,0.0206886,0.01992513],"genre_scores_gemma":[0.5832523,0.006638936,0.3093017,0.005074961,0.002405491,0.001615324,0.06925981,0.008012149,0.01443931],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03016932,"threshold_uncertainty_score":0.1595525,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07354400377569317,"score_gpt":0.3717089619830148,"score_spread":0.2981649582073217,"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."}}