{"id":"W2179179716","doi":"10.1109/rtc.2007.4382793","title":"PESIC: An Integrated Front-End for PET Applications","year":2007,"lang":"en","type":"article","venue":"","topic":"Radiation Detection and Scintillator Technologies","field":"Physics and Astronomy","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"Institute of Particle Physics","funders":"","keywords":"Preamplifier; Photomultiplier; Application-specific integrated circuit; Silicon photomultiplier; Scintillator; Computer science; Front and back ends; Detector; Amplifier; Image resolution; Physics; Optics; CMOS; Electronic engineering; Optoelectronics; Computer hardware; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003174159,0.0004832107,0.0004153732,0.000384003,0.0001645958,0.0006899537,0.001095955,0.0006611216,0.008118005],"category_scores_gemma":[0.0003732133,0.000258175,0.0002060333,0.0004164268,0.0001371497,0.0005768701,0.0003591661,0.0004175683,0.00425741],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003228675,"about_ca_system_score_gemma":0.0003074404,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003029867,"about_ca_topic_score_gemma":0.0003838441,"domain_scores_codex":[0.9997759,0.00002090645,0.00001000914,0.00004938386,0.0001168369,0.00002699903],"domain_scores_gemma":[0.999823,0.00002594796,0.00001394753,0.00002207872,0.00009696716,0.00001801251],"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":[0.002759733,0.0002495186,0.002324046,0.0006629823,0.0001141385,0.001054511,0.000104107,0.01116776,0.6204168,0.01083905,0.03311035,0.3171971],"study_design_scores_gemma":[0.0002386006,0.001807734,0.004936626,0.00009553074,0.0002434465,0.00310333,0.00005252641,0.167662,0.620504,0.002922615,0.1983456,0.00008806867],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.07962452,0.001937645,0.8607346,0.0003659613,0.0004274534,0.0003002135,0.001760579,0.02254618,0.03230288],"genre_scores_gemma":[0.5219874,0.00132024,0.3970085,0.001075449,0.0002680637,0.0002778486,0.00441487,0.0009827166,0.07266484],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.008118005,"threshold_uncertainty_score":0.02715743,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01274489908889926,"score_gpt":0.2766565053575151,"score_spread":0.2639116062686159,"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."}}