{"id":"W2887641423","doi":"10.1109/trpms.2018.2864923","title":"Geometry Optimization of a Dual-Layer Offset Detector for Use in Simultaneous PET/MR Neuroimaging","year":2018,"lang":"en","type":"article","venue":"IEEE Transactions on Radiation and Plasma Medical Sciences","topic":"Medical Imaging Techniques and Applications","field":"Medicine","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Manitoba","funders":"Natural Sciences and Engineering Research Council of Canada; University of Manitoba","keywords":"Detector; Scanner; Image resolution; Optics; Offset (computer science); Physics; Coincidence; Sensitivity (control systems); Materials science; Resolution (logic); Nuclear medicine; Medicine; Computer science; Electronic engineering","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0005077597,0.00009424068,0.0001750082,0.0002905761,0.0001706282,0.00002730106,0.00007435182,0.00006529695,0.0002556107],"category_scores_gemma":[0.000490093,0.00007635147,0.000043318,0.0006078006,0.0004992856,0.0001287209,0.000001493581,0.000162757,0.000003010654],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000250512,"about_ca_system_score_gemma":0.0001308972,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00008082962,"about_ca_topic_score_gemma":0.00005128663,"domain_scores_codex":[0.9987667,0.00004017507,0.0003037796,0.0002722932,0.0004351522,0.0001819508],"domain_scores_gemma":[0.9984772,0.00105504,0.00007531917,0.0001094269,0.00006978749,0.0002132694],"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":[0.0008274172,0.002179227,0.004567879,0.0003758538,0.00008751838,0.00009030534,0.0009210486,0.05623588,0.01451368,0.001065451,0.002878573,0.9162572],"study_design_scores_gemma":[0.0008357058,0.0004413709,0.0002180734,0.0000937388,0.00002647602,0.00006443431,0.0000306323,0.9851482,0.01132958,0.00002505987,0.001704832,0.00008191534],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4225726,0.00001118388,0.5733264,0.003517551,0.0001312236,0.0003307031,0.00002278439,0.00004936188,0.00003823384],"genre_scores_gemma":[0.9693949,0.0002065765,0.02953588,0.0006769602,0.00005821664,0.0000444012,0.000005692935,0.000007352012,0.00006997827],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9289123,"threshold_uncertainty_score":0.3113523,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04142136199675224,"score_gpt":0.3253077358728361,"score_spread":0.2838863738760839,"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."}}