{"id":"W2017685538","doi":"10.1117/12.911662","title":"A liquid xenon detector for PET applications: simulated performance","year":2012,"lang":"en","type":"article","venue":"Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE","topic":"Medical Imaging Techniques and Applications","field":"Medicine","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia; TRIUMF","funders":"Natural Sciences and Engineering Research Council of Canada; TRIUMF","keywords":"Imaging phantom; Detector; Optics; Physics; Xenon; Monte Carlo method; Avalanche photodiode; Full width at half maximum; Image resolution; Iterative reconstruction; Field of view; Computer science; Computer vision; Nuclear physics","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":[0.0004749602,0.0003823967,0.0005226388,0.0002565716,0.0002093173,0.0005630283,0.0007253198,0.0008487777,0.001594056],"category_scores_gemma":[0.001563317,0.00025673,0.0002913087,0.0006377416,0.0003030826,0.0004539953,0.0002351636,0.0002523611,0.0002396572],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009485806,"about_ca_system_score_gemma":0.0006848785,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007071974,"about_ca_topic_score_gemma":0.002855582,"domain_scores_codex":[0.9997727,0.00007655571,0.000009775665,0.00002730808,0.0000802897,0.00003336106],"domain_scores_gemma":[0.999115,0.0005862673,0.00005892884,0.0000426465,0.0001565369,0.00004056517],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0005620848,0.0000856552,0.003905165,0.0001204219,0.00004209754,0.0003374811,0.00006107663,0.9774169,0.01214352,0.0008499486,0.0004651313,0.004010547],"study_design_scores_gemma":[0.00006339671,0.0001474953,0.000941791,0.000007011439,0.00001363489,0.00007099255,0.00001603594,0.9885854,0.00955133,0.0001953265,0.0003973162,0.0000102895],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.955354,0.0005021539,0.03693967,0.0002837444,0.00001763038,0.0000861946,0.0005576921,0.0007978835,0.005461147],"genre_scores_gemma":[0.9885504,0.0001978954,0.009666295,0.00004274199,0.000003307487,0.00006467804,0.0003296494,0.0000714536,0.001073737],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.007071974,"threshold_uncertainty_score":0.01406163,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01531112838148416,"score_gpt":0.2693030128407324,"score_spread":0.2539918844592482,"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."}}