{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001063067,0.0005920037,0.0005583367,0.001625379,0.001104255,0.0009099013,0.0006790024,0.002045314,0.001247633],"category_scores_gemma":[0.001093875,0.0002331521,0.00039356,0.0009642131,0.001020239,0.0007400682,0.0008333161,0.0004606904,0.0005620642],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000554621,"about_ca_system_score_gemma":0.0005182592,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009854006,"about_ca_topic_score_gemma":0.003121772,"domain_scores_codex":[0.9993345,0.0002074114,0.00004459316,0.00008801709,0.0002693508,0.00005613986],"domain_scores_gemma":[0.9994325,0.0002554478,0.00009702705,0.00006550138,0.0001217752,0.00002776088],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.001058979,0.000802725,0.05396846,0.003162114,0.0001186855,0.3081518,0.01222053,0.00928343,0.2339501,0.02843964,0.008094885,0.3407486],"study_design_scores_gemma":[0.00003066706,0.0016301,0.02712761,0.001070134,0.0002037465,0.5256104,0.01061668,0.008245498,0.2702441,0.009367716,0.145677,0.0001763684],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8709975,0.02518837,0.06071321,0.00181224,0.0002507789,0.0005397284,0.0002343123,0.0001250703,0.04013877],"genre_scores_gemma":[0.8805246,0.02693314,0.06808125,0.0004161154,0.000140117,0.0001067505,0.0001554227,0.00008350188,0.02355918],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002045314,"threshold_uncertainty_score":0.005622089,"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."}}