{"id":"W2358952126","doi":"10.12943/cnr.2015.00051","title":"NON-DESTRUCTIVE EXAMINATION USING NEUTRONS: A NUCLEAR WASTE AND ORPHANED SOURCE CHARACTERIZATION CASE STUDY APPLICABLE TO NUCLEAR FORENSICS","year":2015,"lang":"en","type":"article","venue":"AECL Nuclear Review","topic":"Nuclear Physics and Applications","field":"Physics and Astronomy","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Canadian Nuclear Laboratories","funders":"","keywords":"Fissile material; Radioactive waste; Nuclear material; Nuclear engineering; Characterization (materials science); Neutron; Materials science; Radiochemistry; Environmental science; Forensic engineering; Nuclear physics; Chemistry; Physics; Engineering; Nanotechnology","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0002956559,0.0002826586,0.000447323,0.00006397907,0.0003617474,0.00016606,0.0001891413,0.00004253743,0.0001230848],"category_scores_gemma":[0.000006768739,0.0002875071,0.00008309771,0.0004517766,0.00005791367,0.0003415119,0.0002591029,0.0001833727,0.0002813442],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006217949,"about_ca_system_score_gemma":0.00002729501,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003655725,"about_ca_topic_score_gemma":0.000002246648,"domain_scores_codex":[0.9984947,0.00009082538,0.0003820233,0.0005128538,0.000221313,0.0002982235],"domain_scores_gemma":[0.9987491,0.00001715329,0.0002437643,0.0005151959,0.0001779805,0.0002968367],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00009310326,0.002116501,0.001684166,0.001016783,0.0005492789,0.0001254251,0.01920178,0.0002123982,0.03791918,0.07082035,0.00408834,0.8621727],"study_design_scores_gemma":[0.008556401,0.002976424,0.005362721,0.004421734,0.003169558,0.001319672,0.1012048,0.06306861,0.0002873696,0.006064229,0.7979914,0.005577086],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9956022,0.00008200654,0.0008638569,0.0002161068,0.00005236722,0.001947494,0.00002632857,0.0000719902,0.001137664],"genre_scores_gemma":[0.9973843,0.00006870057,0.001498924,0.0005186587,0.0002478933,0.00004454948,0.00003115121,0.0001465147,0.00005934168],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8565956,"threshold_uncertainty_score":0.9999577,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02731286305403233,"score_gpt":0.2776905200720374,"score_spread":0.2503776570180051,"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."}}