{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005177986,0.0007485899,0.000547092,0.00036041,0.000204463,0.0007452093,0.001032786,0.0005222878,0.0007359666],"category_scores_gemma":[0.001251779,0.000668797,0.0003986061,0.0004022384,0.0002325198,0.0005080236,0.0007705931,0.0003534184,0.0003197707],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007403758,"about_ca_system_score_gemma":0.001146419,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001130418,"about_ca_topic_score_gemma":0.002673107,"domain_scores_codex":[0.9996147,0.00007059448,0.00002395382,0.00008197252,0.0001712669,0.00003754314],"domain_scores_gemma":[0.9994994,0.0001485478,0.00010596,0.00007802094,0.0001258281,0.00004220043],"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":[0.0006979256,0.0001478781,0.006812096,0.0005462305,0.0001622183,0.0008357211,0.0001541437,0.09969702,0.8094702,0.0047084,0.00171543,0.07505275],"study_design_scores_gemma":[0.0001282918,0.000848218,0.008318056,0.0000356481,0.0002298727,0.002738157,0.0001043763,0.3826938,0.5873581,0.001764113,0.01558586,0.0001955958],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3029421,0.001468533,0.6890047,0.0003602559,0.00005934488,0.0002301128,0.0004949796,0.001582422,0.003857702],"genre_scores_gemma":[0.4278119,0.0003297803,0.5705176,0.0000717467,0.000006506966,0.0001022923,0.0002565834,0.0001783365,0.0007252468],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001130418,"threshold_uncertainty_score":0.005371869,"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."}}