{"id":"W4236023229","doi":"10.1109/nssmic.2012.6551236","title":"eLine10k: A high dynamic range front-end ASIC for LCLS detectors","year":2012,"lang":"en","type":"article","venue":"","topic":"Particle Detector Development and Performance","field":"Physics and Astronomy","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Sherbrooke","funders":"SLAC National Accelerator Laboratory; Fermilab; Basic Energy Sciences; Office of Science","keywords":"Pixel; Application-specific integrated circuit; Physics; Dynamic range; Correlated double sampling; Shutter; Detector; Rolling shutter; Wide dynamic range; Noise (video); Channel (broadcasting); High dynamic range; Optics; CMOS; Computer science; Computer hardware; Optoelectronics; Telecommunications; Artificial intelligence","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":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0001668263,0.0001520883,0.0001596915,0.00004193783,0.000112969,0.00002745185,0.0001140214,0.00003225778,0.00238014],"category_scores_gemma":[0.000003779292,0.0001277027,0.00008632128,0.00007426168,0.00002004864,0.0002847733,0.00002782415,0.00007102293,0.0004233923],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002588813,"about_ca_system_score_gemma":0.00002411735,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000075945,"about_ca_topic_score_gemma":0.00002026203,"domain_scores_codex":[0.999063,0.00001274622,0.0001886848,0.0001443049,0.0001040308,0.000487212],"domain_scores_gemma":[0.9995748,0.00005551504,0.00005407594,0.0001580123,0.00003217276,0.0001254313],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001646742,0.0002241548,0.8316324,0.00003703436,0.0001959423,2.370064e-7,0.0008265001,0.0000176436,0.009686578,0.003782056,0.003711689,0.1497211],"study_design_scores_gemma":[0.005109453,0.0002195253,0.7606065,0.00003850755,0.0001430261,0.000001606034,0.0004082427,0.01575189,0.1334516,0.002356721,0.08046509,0.00144787],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.981248,0.00007265693,0.01517098,0.00005588606,0.0004876258,0.0002772615,0.00004461948,0.00006692705,0.002576056],"genre_scores_gemma":[0.9942423,0.000001421388,0.003217987,0.00004789845,0.0004244692,0.0001263532,0.00004773918,0.00002219454,0.001869667],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1482732,"threshold_uncertainty_score":0.9985318,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01192822956537197,"score_gpt":0.2485260325976445,"score_spread":0.2365978030322725,"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."}}