{"id":"W2088599564","doi":"10.1016/j.nuclphysbps.2007.07.019","title":"LArGe: Background suppression using liquid argon (LAr) scintillation for 0νββ decay search with enriched germanium (Ge) detectors","year":2007,"lang":"en","type":"article","venue":"Nuclear Physics B - Proceedings Supplements","topic":"Neutrino Physics Research","field":"Physics and Astronomy","cited_by":14,"is_retracted":false,"has_abstract":false,"ca_institutions":"Queen's University","funders":"","keywords":"Semiconductor detector; Germanium; Physics; Scintillation; Liquid scintillation counting; Nuclear physics; Gamma spectroscopy; Spectrometer; Detector; Optics; Radiochemistry; Optoelectronics; Chemistry; Silicon","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":[0.0008921097,0.00046071,0.0006121644,0.0007150757,0.0004378404,0.001125679,0.0007505456,0.0006738202,0.003366144],"category_scores_gemma":[0.0006349589,0.0003352865,0.0003345349,0.0006734183,0.000203323,0.0007561063,0.0008634256,0.0002806829,0.0007643328],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003589547,"about_ca_system_score_gemma":0.0005299485,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007518704,"about_ca_topic_score_gemma":0.002544565,"domain_scores_codex":[0.9995617,0.0001557605,0.00002234813,0.00009898042,0.00008685666,0.00007423911],"domain_scores_gemma":[0.9996458,0.0001255016,0.0000506847,0.00003546305,0.00009610675,0.00004654635],"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":[0.003161096,0.0001424229,0.01684055,0.0001387497,0.0001316119,0.0005508202,0.00009019504,0.001646545,0.9278761,0.003249698,0.001289414,0.0448828],"study_design_scores_gemma":[0.0002308465,0.0008008427,0.007713343,0.00002292424,0.0002566178,0.001291558,0.00009397772,0.03243887,0.9485783,0.0009632529,0.00755245,0.0000570349],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8555124,0.0009813346,0.1217778,0.0004396148,0.00004207428,0.0001175341,0.0006231904,0.002596877,0.01790912],"genre_scores_gemma":[0.939334,0.0002086006,0.0565444,0.000169222,0.00001651327,0.0000381305,0.0005610755,0.0001713661,0.002956814],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.003366144,"threshold_uncertainty_score":0.01126087,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03678562570348678,"score_gpt":0.3327746692561092,"score_spread":0.2959890435526224,"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."}}