{"id":"W2408893109","doi":"10.1109/tmi.2016.2577539","title":"Improving Depth, Energy and Timing Estimation in PET Detectors with Deconvolution and Maximum Likelihood Pulse Shape Discrimination","year":2016,"lang":"en","type":"article","venue":"IEEE Transactions on Medical Imaging","topic":"Radiation Detection and Scintillator Technologies","field":"Physics and Astronomy","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Cancer Institute; Natural Sciences and Engineering Research Council of Canada; National Institutes of Health","keywords":"Scintillator; Deconvolution; Detector; Scintillation; Photoelectric effect; Optics; Photodetector; Waveform; Lyso-; Physics; Energy (signal processing); Pulse (music); Scintillation counter; Voltage","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001965715,0.0006982041,0.0004679369,0.0006734472,0.0001640082,0.0008637884,0.0007029407,0.0008375847,0.0004639919],"category_scores_gemma":[0.005651721,0.0005777557,0.0006183108,0.0005667547,0.0004606169,0.001579687,0.0009601374,0.001065489,0.0003233813],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009706041,"about_ca_system_score_gemma":0.001076024,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001266317,"about_ca_topic_score_gemma":0.001504926,"domain_scores_codex":[0.9995273,0.0001264999,0.00003761864,0.00007307732,0.0002033039,0.00003216643],"domain_scores_gemma":[0.99847,0.0009180642,0.0002682062,0.0001046717,0.0001957551,0.00004321868],"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":[0.0007501732,0.0001765453,0.006180283,0.000315541,0.0001082875,0.0003061361,0.0003864755,0.3069846,0.421499,0.01503568,0.0004942418,0.2477631],"study_design_scores_gemma":[0.00001143674,0.00004530316,0.0006385377,0.00001021759,0.00001086141,0.0001207041,0.0000108126,0.9058963,0.09070977,0.00188489,0.0006217016,0.00003949882],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02567459,0.0001248139,0.9735034,0.00004321373,0.000005692342,0.00001359165,0.00002260653,0.0004023736,0.0002096726],"genre_scores_gemma":[0.2322221,0.0002063783,0.7663928,0.00005863032,0.00001130744,0.00005387928,0.0001044452,0.000156956,0.0007934627],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001965715,"threshold_uncertainty_score":0.01039582,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006592873050510398,"score_gpt":0.227680858457929,"score_spread":0.2210879854074186,"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."}}