{"id":"W4382794486","doi":"10.1016/j.nima.2023.168503","title":"An ultrasonic approach to identify in-core reactor fuel for safeguards applications","year":2023,"lang":"en","type":"article","venue":"Nuclear Instruments and Methods in Physics Research Section A Accelerators Spectrometers Detectors and Associated Equipment","topic":"Nuclear Physics and Applications","field":"Physics and Astronomy","cited_by":1,"is_retracted":false,"has_abstract":false,"ca_institutions":"Canadian Nuclear Laboratories","funders":"","keywords":"Ultrasonic sensor; Identification (biology); Computer science; Core (optical fiber); Nuclear reactor core; Process engineering; Work (physics); Encoding (memory); Nuclear engineering; Acoustics; Mechanical engineering; Engineering; Telecommunications; Artificial intelligence; Physics","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":[0.000379416,0.0006163503,0.0003861179,0.001365818,0.0003617642,0.0008371542,0.0007694058,0.001013638,0.003772074],"category_scores_gemma":[0.0007471887,0.000334306,0.0003226785,0.0007587011,0.0003683058,0.0008443431,0.0007997612,0.0005915978,0.001385903],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003788618,"about_ca_system_score_gemma":0.0004931203,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008893967,"about_ca_topic_score_gemma":0.002001082,"domain_scores_codex":[0.9997031,0.00003780639,0.000013832,0.00006710728,0.0001444191,0.0000337086],"domain_scores_gemma":[0.9995971,0.0001063497,0.00005066276,0.0000471943,0.0001767055,0.00002199298],"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":[0.0001606166,0.00007961359,0.001677243,0.0001714623,0.00002248568,0.00008651159,0.00008680291,0.001573166,0.8952718,0.002379262,0.0006686861,0.09782243],"study_design_scores_gemma":[0.00003524641,0.0006499966,0.004216449,0.00006162563,0.0001416414,0.0006810307,0.000308998,0.07718331,0.8972328,0.001608668,0.0178214,0.00005879988],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1022628,0.002891395,0.8769796,0.0004799376,0.0004425273,0.0002636385,0.0004171595,0.001195493,0.01506743],"genre_scores_gemma":[0.448754,0.002696724,0.5284015,0.0004825309,0.0001814543,0.0002892841,0.0003342354,0.0001652358,0.01869505],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003772074,"threshold_uncertainty_score":0.01261884,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08256475233415979,"score_gpt":0.4416558690875212,"score_spread":0.3590911167533614,"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."}}