{"id":"W4380993800","doi":"10.48550/arxiv.2306.08661","title":"Energy Calibration of Germanium Detectors for the MAJORANA DEMONSTRATOR","year":2023,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Neutrino Physics Research","field":"Physics and Astronomy","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","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; Germanium; Calibration; Semiconductor detector; Detector; Double beta decay; Physics; Nuclear physics; Energy (signal processing); Remote sensing; Optics; Neutrino; Optoelectronics; Geology","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":[0.003416446,0.00150636,0.0008552122,0.001803146,0.001251804,0.00176151,0.002708736,0.001943007,0.007779616],"category_scores_gemma":[0.004945779,0.00124048,0.0007538559,0.002173591,0.0005291788,0.00112402,0.002006903,0.001580926,0.003586648],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001525728,"about_ca_system_score_gemma":0.0007578625,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000637924,"about_ca_topic_score_gemma":0.001807261,"domain_scores_codex":[0.9967384,0.0005199892,0.0001640578,0.001213406,0.00101858,0.0003456332],"domain_scores_gemma":[0.9973941,0.0005590038,0.0002346706,0.0008703005,0.0008510567,0.000090847],"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":[0.00241792,0.0003923062,0.02369376,0.0003930855,0.000231874,0.0004870038,0.0007300876,0.01049355,0.823568,0.01624979,0.009482563,0.1118602],"study_design_scores_gemma":[0.0001212286,0.000554793,0.01687881,0.000055237,0.0001269714,0.0007364555,0.0001192522,0.01931148,0.9010546,0.002652306,0.05826049,0.0001283885],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3332701,0.0007300931,0.6202257,0.000640051,0.000458322,0.0007572999,0.002919541,0.009744779,0.03125416],"genre_scores_gemma":[0.5695545,0.0003850868,0.4114698,0.0004174186,0.00006702797,0.0008486793,0.003391952,0.002978564,0.01088699],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.007779616,"threshold_uncertainty_score":0.02602547,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0936735321446998,"score_gpt":0.2204562944740585,"score_spread":0.1267827623293587,"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."}}