{"id":"W4386769850","doi":"10.1088/1748-0221/18/09/p09023","title":"Energy calibration of germanium detectors for the Majorana Demonstrator","year":2023,"lang":"en","type":"article","venue":"Journal of Instrumentation","topic":"Neutrino Physics Research","field":"Physics and Astronomy","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"Queen's University","funders":"Pacific Northwest National Laboratory; Natural Sciences and Engineering Research Council of Canada; Nuclear Physics; Los Alamos National Laboratory; L'Oreal USA; Oak Ridge National Laboratory; South Dakota Board of Regents; Lawrence Berkeley National Laboratory; Laboratory Directed Research and Development; U.S. Department of Energy; Office of Science; National Science Foundation","keywords":"MAJORANA; Calibration; Germanium; Detector; Semiconductor detector; Double beta decay; Physics; Energy (signal processing); Nuclear physics; Remote sensing; Instrumentation (computer programming); Optics; Computer science; Geology; Optoelectronics; Neutrino","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001980678,0.00005365137,0.0001007361,0.0001031274,0.00007093676,0.00002944052,0.000117286,0.00001392168,0.00003303552],"category_scores_gemma":[0.000001045916,0.00003826844,0.0001075423,0.0002500429,0.00002773402,0.0002430459,0.00001448571,0.00006602331,0.000001313321],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001939358,"about_ca_system_score_gemma":0.00008724158,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004593127,"about_ca_topic_score_gemma":0.000003701219,"domain_scores_codex":[0.9992936,0.00003337302,0.0002988015,0.00004874629,0.0002248189,0.0001006745],"domain_scores_gemma":[0.999295,0.0001504806,0.0003172118,0.00006863091,0.0001376383,0.00003103117],"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":[0.0002526469,0.0001280644,0.02832596,0.00005027395,0.0004289647,0.00000152984,0.0005533706,0.001684986,0.582576,0.1140661,0.002478722,0.2694535],"study_design_scores_gemma":[0.0007235099,0.0001233498,0.00349613,0.00001530994,0.00003494228,0.000001123149,0.0007735118,0.006016825,0.9855403,0.002944798,0.0002805876,0.00004960237],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9358138,0.00001204972,0.06300395,0.0002969748,0.000268923,0.0001410124,0.0000203638,0.000005246514,0.0004377051],"genre_scores_gemma":[0.9995264,0.000007389466,0.0001009481,0.00001156478,0.0002843779,0.00001064862,0.00001104085,0.000008621327,0.0000389596],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4029644,"threshold_uncertainty_score":0.1560542,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02108548670330241,"score_gpt":0.3032310588707348,"score_spread":0.2821455721674324,"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."}}