{"id":"W4391614760","doi":"10.22323/1.441.0250","title":"$^{76}$Ge Detectors of LEGEND experiment: Production, Characterization, Performance","year":2024,"lang":"en","type":"article","venue":"","topic":"Neutrino Physics Research","field":"Physics and Astronomy","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Los Alamos National Laboratory; Lawrence Berkeley National Laboratory; Oak Ridge National Laboratory; Laboratory Directed Research and Development; Science and Technology Facilities Council; Natural Sciences and Engineering Research Council of Canada; Deutsche Forschungsgemeinschaft; Ministerstwo Edukacji i Nauki; Max-Planck-Gesellschaft; Agentúra na Podporu Výskumu a Vývoja; Bundesministerium für Bildung und Forschung; Russian Foundation for Basic Research; Ministerstvo Školství, Mládeže a Tělovýchovy; U.S. Department of Energy; Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung; National Science Foundation","keywords":"Germanium; Detector; Characterization (materials science); Legend; Semiconductor detector; Physics; MAJORANA; Nuclear physics; Optoelectronics; Optics; Silicon; Neutrino; Geography","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":[0.001972686,0.0009496228,0.000692374,0.001093088,0.0003263284,0.001300264,0.001297932,0.001201277,0.003019773],"category_scores_gemma":[0.001183129,0.0005096503,0.000499179,0.001279013,0.000548692,0.001125155,0.0008021967,0.0006937945,0.002159357],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001180257,"about_ca_system_score_gemma":0.0005811398,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00153693,"about_ca_topic_score_gemma":0.00158556,"domain_scores_codex":[0.9988329,0.000179691,0.00003007718,0.0003170058,0.0005125861,0.0001277137],"domain_scores_gemma":[0.9995402,0.00007421181,0.00005534369,0.000118529,0.0001670473,0.00004476414],"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":[0.003900581,0.0002513799,0.02182317,0.0009888026,0.0003057242,0.0005511207,0.0004171333,0.004886547,0.8411006,0.008053508,0.02622737,0.09149399],"study_design_scores_gemma":[0.0002256558,0.001422106,0.03085859,0.0001043174,0.0002507166,0.001603562,0.0001186126,0.009873034,0.8345017,0.001054337,0.1197,0.000287419],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.759549,0.01023056,0.1151031,0.001169775,0.0004105565,0.0006523907,0.02979311,0.01341354,0.06967801],"genre_scores_gemma":[0.8536168,0.001949451,0.09823515,0.0004432314,0.0001386869,0.0002795647,0.02896065,0.001788928,0.01458747],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.003019773,"threshold_uncertainty_score":0.01043272,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01449961128815333,"score_gpt":0.2765019838915688,"score_spread":0.2620023726034155,"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."}}