{"id":"W2090696038","doi":"10.1002/mrm.23087","title":"Robust multipoint water‐fat separation using fat likelihood analysis","year":2011,"lang":"en","type":"article","venue":"Magnetic Resonance in Medicine","topic":"Advanced MRI Techniques and Applications","field":"Medicine","cited_by":27,"is_retracted":false,"has_abstract":true,"ca_institutions":"Western University","funders":"National Institute of Diabetes and Digestive and Kidney Diseases","keywords":"Ambiguity; Smoothness; Computer science; Separation (statistics); Field (mathematics); Algorithm; Pattern recognition (psychology); Artificial intelligence; Biological system; Mathematics; Machine learning; Biology","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":[0.001759235,0.001116452,0.0009941979,0.001837759,0.0005823608,0.001140284,0.001308064,0.001184726,0.001996469],"category_scores_gemma":[0.004881967,0.0006088275,0.001236839,0.001018188,0.0007289845,0.001815245,0.002019153,0.001401902,0.00116695],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004586713,"about_ca_system_score_gemma":0.001025161,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001228975,"about_ca_topic_score_gemma":0.001575675,"domain_scores_codex":[0.9993274,0.000180299,0.00003662846,0.0001432815,0.000248075,0.00006438226],"domain_scores_gemma":[0.9987547,0.0006889557,0.0001905156,0.0001483052,0.0001695596,0.00004799476],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0007398512,0.000169317,0.003015938,0.0002906224,0.0002424148,0.0005138703,0.0003630165,0.2573546,0.125791,0.01758456,0.002217105,0.5917177],"study_design_scores_gemma":[0.00002504963,0.00005520727,0.001072263,0.0000147086,0.00002689156,0.0001918704,0.00002939865,0.9620326,0.0252243,0.009250761,0.002021836,0.00005507979],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.005204753,0.00007895457,0.993975,0.00004116563,0.00000554448,0.00001579209,0.00002644475,0.0004899762,0.0001624441],"genre_scores_gemma":[0.1168044,0.0001369386,0.8813759,0.00004928319,0.00002438006,0.00008032635,0.0002390476,0.0003439809,0.0009457455],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001996469,"threshold_uncertainty_score":0.009303808,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07009120710725765,"score_gpt":0.337829305098975,"score_spread":0.2677380979917173,"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."}}