{"id":"W2911986129","doi":"10.1016/j.jallcom.2019.01.364","title":"Microstructure and mechanical properties of fine-grained aluminum matrix composite reinforced with nitinol shape memory alloy particulates produced by underwater friction stir processing","year":2019,"lang":"en","type":"article","venue":"Journal of Alloys and Compounds","topic":"Innovations in Concrete and Construction Materials","field":"Engineering","cited_by":82,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Waterloo","funders":"Priority Academic Program Development of Jiangsu Higher Education Institutions; Nanjing University of Aeronautics and Astronautics; National Natural Science Foundation of China","keywords":"Materials science; Microstructure; Shape-memory alloy; Composite material; Austenite; Friction stir processing; Martensite; Composite number; Dynamic recrystallization; Alloy; Metallurgy","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":[0.0001540457,0.0001524713,0.0003056666,0.00009147126,0.00007070072,0.0001116997,0.00007158791,0.00009625024,0.00001993013],"category_scores_gemma":[0.000005193625,0.0001068139,0.0000292261,0.0001065364,0.0000665336,0.0002777262,0.00002352832,0.0001782099,5.217723e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001936232,"about_ca_system_score_gemma":0.00002902607,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000003156045,"about_ca_topic_score_gemma":4.235061e-7,"domain_scores_codex":[0.9990981,0.00002173043,0.0004676831,0.0001086674,0.0001653992,0.0001384737],"domain_scores_gemma":[0.9994067,0.00001276486,0.0002078228,0.00008550198,0.0002466502,0.00004056493],"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":[0.0001482535,0.000004329233,0.0002688387,0.0002520024,0.00009435211,0.000001967755,0.0002242375,0.001235346,0.9971439,0.0000958145,0.0001074566,0.000423542],"study_design_scores_gemma":[0.001372463,0.0003214588,0.0003478965,0.0003892161,0.0000886416,0.0005676962,0.0004301801,0.03057771,0.9652627,0.00006607417,0.0003331842,0.0002428152],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9988702,0.0003857265,0.0002139913,0.0001536851,0.0001653194,0.0001419467,0.00000405655,0.00002883766,0.00003624649],"genre_scores_gemma":[0.9979182,0.00003566121,0.001842631,0.00003314593,0.00007295084,0.000002618043,0.000004318551,0.00001956345,0.0000708902],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0318812,"threshold_uncertainty_score":0.4355744,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007053598708888525,"score_gpt":0.195123994695336,"score_spread":0.1880703959864475,"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."}}