{"id":"W6929625919","doi":"10.48550/arxiv.nucl-ex/0606022","title":"Performance of HPGe Detectors in High Magnetic Fields","year":2006,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Genomics and Rare Diseases","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Hyperion Technologies (Canada)","funders":"","keywords":"Semiconductor detector; Spectrometer; Detector; Preamplifier; Germanium; Resolution (logic); Nuclear electronics; Proton","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":[0.001925668,0.0006390961,0.0007840018,0.001010803,0.0006071013,0.001403804,0.001233079,0.001380226,0.002625718],"category_scores_gemma":[0.00360009,0.0006025545,0.0002237466,0.000730083,0.0005877874,0.0009138145,0.0009337065,0.0005114086,0.0009399788],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007995208,"about_ca_system_score_gemma":0.0003272132,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001184719,"about_ca_topic_score_gemma":0.0007532416,"domain_scores_codex":[0.9984984,0.0004572991,0.00007049365,0.0004233482,0.00032622,0.0002241521],"domain_scores_gemma":[0.9971418,0.001647901,0.0002137244,0.0003306635,0.0004976621,0.0001682491],"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":[0.003279867,0.0001075505,0.00580154,0.0002492494,0.0001244878,0.0004319208,0.000415953,0.003266709,0.9654993,0.001072037,0.0007666586,0.01898452],"study_design_scores_gemma":[0.0001034243,0.0008598033,0.006907061,0.00002941322,0.00007812722,0.0004835919,0.0001036059,0.00712447,0.9786147,0.0003847622,0.005268602,0.00004233125],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9740122,0.001794493,0.0185682,0.0002767677,0.00009707607,0.00003959536,0.000365971,0.001185541,0.003660213],"genre_scores_gemma":[0.9842244,0.0004026305,0.01150064,0.00009784657,0.00003078818,0.00003102858,0.0004427128,0.0001839406,0.003085987],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002625718,"threshold_uncertainty_score":0.01018399,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01562186778414634,"score_gpt":0.1504245833482101,"score_spread":0.1348027155640637,"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."}}