{"id":"W4200360657","doi":"10.1016/j.jnucmat.2021.153484","title":"The microstructure, mechanical-thermal properties and softening resistance of Y4Al2O9 dispersion-strengthened Cu alloy","year":2021,"lang":"en","type":"article","venue":"Journal of Nuclear Materials","topic":"Fusion materials and technologies","field":"Materials Science","cited_by":26,"is_retracted":false,"has_abstract":false,"ca_institutions":"Smarter Alloys (Canada)","funders":"National Magnetic Confinement Fusion Program of China; Fundamental Research Funds for the Central Universities; Huazhong University of Science and Technology; Ministry of Science and Technology of the People's Republic of China","keywords":"Materials science; Alloy; Microstructure; Nanoparticle; Spark plasma sintering; Oxide; Volume fraction; Indentation hardness; Metallurgy; Composite material; Nanotechnology","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":[],"consensus_categories":[],"category_scores_codex":[0.0007765535,0.0001481354,0.0004320405,0.00003934404,0.0002479645,0.0003042354,0.0003792683,0.0001089624,0.0006241071],"category_scores_gemma":[0.0004777113,0.0000839225,0.0000707943,0.00007045704,0.0002572871,0.0001809744,0.0003042953,0.00008401847,0.000009254399],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001746298,"about_ca_system_score_gemma":0.00005277666,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000008112755,"about_ca_topic_score_gemma":0.000006183404,"domain_scores_codex":[0.9984082,0.0001826606,0.0007348961,0.0001644606,0.0002882004,0.0002215968],"domain_scores_gemma":[0.998661,0.00009176249,0.0006672994,0.0002659641,0.0002631936,0.00005074759],"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":[0.0003867426,0.00001961821,0.000007507197,0.00007642562,0.00001881264,0.00002876911,0.0001541447,0.000001774941,0.9971274,0.001413243,0.0004823699,0.0002832242],"study_design_scores_gemma":[0.000357884,0.00008691601,0.002549305,0.0002415365,0.00002905373,0.0001121275,0.0007432376,0.000002117193,0.9883939,0.001173513,0.006198697,0.0001117353],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9965283,0.001211551,8.212173e-7,0.001154979,0.0009471403,0.00006576299,0.00003007465,0.00002762142,0.00003378767],"genre_scores_gemma":[0.9940018,0.0008561986,0.004812193,0.00008143845,0.0001207776,0.000001082904,5.375263e-7,0.00002469202,0.0001012735],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.00873349,"threshold_uncertainty_score":0.6833535,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0159204560565972,"score_gpt":0.2089029696322494,"score_spread":0.1929825135756522,"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."}}