{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005273261,0.00009056891,0.0001012974,0.00006078469,0.0007542728,0.0000852997,0.00008683489,0.0000145819,7.210252e-7],"category_scores_gemma":[0.00022671,0.00004411389,0.0000168297,0.0005352207,0.0006290881,0.0003438765,0.00007955956,0.000112209,0.000003403843],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001952366,"about_ca_system_score_gemma":0.00004237082,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000004360732,"about_ca_topic_score_gemma":0.000004139129,"domain_scores_codex":[0.9990098,0.00000991948,0.0001085897,0.0003638269,0.0001792617,0.0003285895],"domain_scores_gemma":[0.9991469,0.0003010425,0.00004039257,0.0002766155,0.0001610663,0.00007395328],"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":[0.0001324626,0.00001685402,0.001706174,0.00002015791,0.00000257252,0.000009484388,0.0001302137,0.00005731718,0.7944099,0.001898982,0.00009817658,0.2015177],"study_design_scores_gemma":[0.000475063,0.000210479,0.007974968,0.0001429204,0.00002331628,0.00008274629,0.0003452014,0.02703654,0.8690709,0.05276141,0.04165204,0.0002244319],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7023566,0.0002792492,0.2724381,0.02262595,0.0001551179,0.001390603,0.00001474272,0.0006400945,0.00009958113],"genre_scores_gemma":[0.9367836,0.000156658,0.06234461,0.0003562889,0.00005475476,0.0001191851,0.000005093686,0.00001328287,0.0001665719],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.234427,"threshold_uncertainty_score":0.5801333,"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."}}