{"id":"W4360618282","doi":"10.1002/maco.202313763","title":"An overview of the Canadian nuclear waste corrosion program","year":2023,"lang":"en","type":"article","venue":"Materials and Corrosion","topic":"Hydrogen embrittlement and corrosion behaviors in metals","field":"Materials Science","cited_by":14,"is_retracted":false,"has_abstract":true,"ca_institutions":"Nuclear Waste Management Organization","funders":"Natural Sciences and Engineering Research Council of Canada; University of Toronto; York University; Nationale Genossenschaft für die Lagerung radioaktiver Abfälle; Nuclear Waste Management Organization","keywords":"Corrosion; Radioactive waste; Spent nuclear fuel; Allowance (engineering); Work (physics); Environmental science; High-level waste; Waste management; Forensic engineering; Engineering; Metallurgy; Materials science; Operations management","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0008309066,0.0001775057,0.0002824643,0.00009169539,0.0005664849,0.0002227411,0.0004135956,0.0001185632,0.001176081],"category_scores_gemma":[0.00002628654,0.0001201626,0.00005875071,0.0003187115,0.0001479455,0.0001703324,0.0002065243,0.0000585446,0.0001106625],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003208593,"about_ca_system_score_gemma":0.00006442711,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005637749,"about_ca_topic_score_gemma":0.00554204,"domain_scores_codex":[0.9982945,0.0002224192,0.0003891341,0.0003357003,0.0003789268,0.0003792776],"domain_scores_gemma":[0.9990878,0.00001745953,0.0001643416,0.0004818175,0.00006720398,0.0001813405],"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":[0.00001964166,0.00004463164,0.000745847,0.00004922796,2.545449e-7,0.00000288328,0.0001389555,0.000003023547,0.9951979,0.001031945,0.0007725678,0.001993107],"study_design_scores_gemma":[0.000240871,0.0002095436,0.003943603,0.0001638923,0.00003372996,0.000007121997,0.0001700861,0.0001025793,0.9900861,0.0005322521,0.004313965,0.0001962703],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9973742,0.00009167331,0.00000203541,0.0001231669,0.001570542,0.0005152955,0.0001111805,0.0001371312,0.00007475385],"genre_scores_gemma":[0.9991287,0.0001982536,0.0001598558,0.0001442565,0.00007139384,0.00004758515,0.00002972193,0.00003083473,0.0001894116],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.00511183,"threshold_uncertainty_score":0.999737,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04454312498888722,"score_gpt":0.3073408727152003,"score_spread":0.2627977477263131,"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."}}