{"id":"W4388173883","doi":"10.1126/sciadv.adh9853","title":"The CALIPR framework for highly accelerated myelin water imaging with improved precision and sensitivity","year":2023,"lang":"en","type":"article","venue":"Science Advances","topic":"Advanced Neuroimaging Techniques and Applications","field":"Medicine","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"Philips (Canada); International Collaboration On Repair Discoveries; University of British Columbia","funders":"","keywords":"Myelin; Magnetic resonance imaging; Computer science; Context (archaeology); Spinal cord; Biomedical engineering; Artificial intelligence; Radiology; Medicine; Neuroscience; Biology; Central nervous system","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":[0.001846306,0.001083041,0.0007362317,0.000763853,0.0003366676,0.001508871,0.002067483,0.0008763302,0.003965011],"category_scores_gemma":[0.003343523,0.0004447071,0.0008852956,0.0007057556,0.0007764184,0.001200206,0.002283906,0.001979241,0.001938033],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00057589,"about_ca_system_score_gemma":0.001415364,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002413278,"about_ca_topic_score_gemma":0.004215974,"domain_scores_codex":[0.9990632,0.0002046885,0.00004352122,0.000170688,0.0004467368,0.00007119569],"domain_scores_gemma":[0.9992518,0.0002080402,0.0001045726,0.0001720211,0.0001961748,0.00006730112],"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":[0.0005861963,0.0001567675,0.001114407,0.0008817817,0.0002427993,0.0006747508,0.0003553462,0.1692593,0.2295832,0.1973029,0.01903433,0.3808083],"study_design_scores_gemma":[0.00006243058,0.0002235529,0.0005891436,0.00006342443,0.00005215509,0.0006713005,0.00004416757,0.8495972,0.04726972,0.04013299,0.06119158,0.0001023635],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.001700512,0.0001943342,0.9956015,0.00008134933,0.00002179355,0.00002784113,0.0001183275,0.001459502,0.0007948083],"genre_scores_gemma":[0.03373889,0.0004183416,0.9626265,0.0001357404,0.0000482959,0.0001636761,0.0006265193,0.0008197002,0.001422307],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.003965011,"threshold_uncertainty_score":0.0132643,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04572890420358438,"score_gpt":0.3791686319174054,"score_spread":0.333439727713821,"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."}}