{"id":"W2808959108","doi":"10.1016/j.nme.2018.05.018","title":"Micro mechanical testing of candidate structural alloys for Gen-IV nuclear reactors","year":2018,"lang":"en","type":"article","venue":"Nuclear Materials and Energy","topic":"Fusion materials and technologies","field":"Materials Science","cited_by":45,"is_retracted":false,"has_abstract":true,"ca_institutions":"Queen's University","funders":"Engineering and Physical Sciences Research Council; U.S. Department of Energy; Behavioral Neuroscience and Comparative Psychology; Basic Energy Sciences; Office of Nuclear Energy; Office of Science; International Foundation for Research in Paraplegia","keywords":"Nanoindentation; Nanocrystalline material; Materials science; Irradiation; Austenite; Metallurgy; Hardening (computing); Oxide; Composite material; Microstructure; Nanotechnology; Nuclear 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":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0002838719,0.0002084968,0.0004168986,0.00006266256,0.0002525673,0.0001707964,0.0002972322,0.0001894769,0.001744185],"category_scores_gemma":[0.0001579086,0.0001643874,0.00003996328,0.00007363076,0.0002880799,0.0001324012,0.0003191894,0.00002885981,0.00002557218],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001672817,"about_ca_system_score_gemma":0.00001694541,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001023339,"about_ca_topic_score_gemma":0.00003006233,"domain_scores_codex":[0.9986324,0.00005035493,0.0004373576,0.0003805916,0.0001355268,0.0003638043],"domain_scores_gemma":[0.9992074,0.00005658489,0.0002407438,0.0002962778,0.0001258808,0.00007307116],"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":[0.0001923032,0.00001107408,0.000005960752,0.00005346525,0.000008389813,0.00000179449,0.00006950612,4.278974e-7,0.9744136,0.02351491,0.0005614777,0.001167137],"study_design_scores_gemma":[0.0004171754,0.0004330702,0.0006388937,0.00004661822,0.0000210997,0.00002262092,0.00009187855,0.00008589914,0.9706073,0.003564423,0.02384041,0.0002306326],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9980347,0.00002304126,0.000005938833,0.0001170286,0.001070337,0.00007722194,0.0001828379,0.0002064179,0.0002824646],"genre_scores_gemma":[0.9849068,0.00003887408,0.01445352,0.0001583168,0.0003243754,0.000006460324,0.00000966181,0.00005559644,0.00004639274],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02327893,"threshold_uncertainty_score":0.9991683,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0176974289444758,"score_gpt":0.2277806652186538,"score_spread":0.210083236274178,"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."}}