{"id":"W2891998940","doi":"10.1016/j.jmir.2018.06.042","title":"Can MRT Practice Advance 3D Printing?","year":2018,"lang":"en","type":"article","venue":"Journal of medical imaging and radiation sciences","topic":"Anatomy and Medical Technology","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":false,"ca_institutions":"London Health Sciences Centre; Western University; Sunnybrook Health Science Centre; Health Sciences Centre; University Health Network","funders":"","keywords":"3D printing; Imaging phantom; 3d printed; Medical physics; DICOM; 3d printer; Computer science; Medical imaging; Medicine; Radiology; Biomedical engineering; Engineering; Mechanical engineering","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":[0.001497799,0.0000460477,0.00009630541,0.0001102145,0.0001000241,0.00002939696,0.0001869826,0.00004571529,0.00004409417],"category_scores_gemma":[0.002699484,0.00003230791,0.00001571098,0.0002122395,0.0006269494,0.0002777154,0.00001899214,0.0002468426,0.000002873378],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001568846,"about_ca_system_score_gemma":0.00009254701,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000004392828,"about_ca_topic_score_gemma":0.000001670464,"domain_scores_codex":[0.9989898,0.0000240051,0.000216247,0.00006716156,0.0005675086,0.0001352805],"domain_scores_gemma":[0.9994315,0.0001995995,0.00009640725,0.00003714118,0.00007780423,0.0001575665],"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":[0.000002174382,0.0000114072,0.00125925,0.000008586074,0.000009778528,0.00002914575,0.0003704773,0.00002123192,0.00005291435,0.001736477,0.00510519,0.9913934],"study_design_scores_gemma":[0.0003650845,0.00007917489,0.001206429,0.0001048963,0.0000156682,0.0009151264,0.001821131,0.08908957,0.0004693086,0.00133803,0.9045084,0.00008723296],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.3780741,0.06509628,0.1690431,0.3484504,0.009890851,0.000158779,0.000001836868,0.0003804084,0.02890428],"genre_scores_gemma":[0.9857118,0.006359983,0.00593471,0.001293374,0.0006850154,3.911462e-7,6.292911e-8,0.000003208483,0.00001150628],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9913061,"threshold_uncertainty_score":0.3231729,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006824743798937475,"score_gpt":0.3093259744294053,"score_spread":0.3025012306304679,"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."}}