{"id":"W2254864317","doi":"10.1016/j.apradiso.2016.06.032","title":"A precise method to determine the activity of a weak neutron source using a germanium detector","year":2016,"lang":"en","type":"article","venue":"Applied Radiation and Isotopes","topic":"Nuclear Physics and Applications","field":"Physics and Astronomy","cited_by":6,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Foundation for Innovation","keywords":"Semiconductor detector; Germanium; Physics; Beryllium; Neutron; Neutron source; Nuclear physics; Monte Carlo method; Detector; Gamma ray; Neutron activation analysis; Gamma spectroscopy; Neutron detection; Bonner sphere; Radiochemistry; Neutron temperature; Neutron cross section; Optics; Chemistry; Optoelectronics","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":[0.00009362039,0.00008693626,0.0001180206,0.00002859869,0.0001093751,0.00002349386,0.00008370423,0.00001685578,0.00005479008],"category_scores_gemma":[9.807621e-7,0.00005328561,0.00003888608,0.0001039162,0.00002774191,0.00005575863,0.00004745978,0.00003954504,0.00001198508],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000009563653,"about_ca_system_score_gemma":0.00001545871,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006835278,"about_ca_topic_score_gemma":0.000002130292,"domain_scores_codex":[0.9995263,0.00002522281,0.0001099548,0.0001577461,0.00006826196,0.0001125344],"domain_scores_gemma":[0.9995557,0.0001041572,0.00009507784,0.0001738928,0.00001936522,0.00005177167],"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":[0.00001340737,0.00002698712,0.0001641692,0.000002256913,0.00001393258,5.723949e-9,0.0001532982,0.00003080851,0.5599846,0.01469938,0.00005486866,0.4248562],"study_design_scores_gemma":[0.001361152,0.00007542294,0.02914015,0.00003063376,0.0001053802,0.000001082624,0.0002428585,0.005838967,0.8150005,0.003933303,0.1438498,0.0004207582],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.822197,0.000006806317,0.1750077,0.0003785423,0.00001507711,0.0003955914,0.00002650433,0.00001343418,0.001959363],"genre_scores_gemma":[0.9976695,0.000001764352,0.00190067,0.00004905961,0.0001496971,0.00007258129,9.70439e-7,0.00001320551,0.0001425237],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4244355,"threshold_uncertainty_score":0.2172924,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01344902556533519,"score_gpt":0.2643902103170102,"score_spread":0.250941184751675,"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."}}