{"id":"W2791545517","doi":"10.1117/12.2293566","title":"Design and evaluation of a diffusion MRI fibre phantom using 3D printing","year":2018,"lang":"en","type":"article","venue":"Medical Imaging 2018: Physics of Medical Imaging","topic":"Advanced Neuroimaging Techniques and Applications","field":"Medicine","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"Robarts Clinical Trials","funders":"","keywords":"Imaging phantom; 3D printing; Diffusion; Computer science; Biomedical engineering; Materials science; Engineering; Optics; Physics; Composite material","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":[0.00302338,0.0002669789,0.0005561115,0.0001496729,0.0001629777,0.00002291481,0.0003543336,0.0000985483,0.0002047092],"category_scores_gemma":[0.001483531,0.0002345165,0.00009995949,0.0004196306,0.00153346,0.000256133,0.0004433443,0.0005243641,0.000004139894],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000771612,"about_ca_system_score_gemma":0.0005207501,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001001539,"about_ca_topic_score_gemma":5.278529e-7,"domain_scores_codex":[0.9948555,0.0001980822,0.000745501,0.0005586264,0.003209008,0.0004332328],"domain_scores_gemma":[0.9975991,0.0003436352,0.0003944623,0.0005410291,0.0006581495,0.0004636254],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00006510853,0.0005368111,0.01458854,0.0003472008,0.00005183935,0.00003234548,0.0005354668,0.00002545802,0.0802704,0.0005186013,0.001306189,0.901722],"study_design_scores_gemma":[0.001863834,0.00004573105,0.001074386,0.002146858,0.0002831137,0.0002111065,0.00008993047,0.9623891,0.02682339,0.004153318,0.0007134126,0.0002058384],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1413323,0.0005494766,0.8510073,0.005859313,0.0001505337,0.0006048722,0.00000289153,0.0001400683,0.0003533104],"genre_scores_gemma":[0.8897694,0.0002020026,0.1083921,0.0008788143,0.0006480087,0.00002640304,0.00001278972,0.00006059735,0.000009797059],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9623636,"threshold_uncertainty_score":0.9563307,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09184233710314892,"score_gpt":0.4189484282336127,"score_spread":0.3271060911304638,"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."}}