{"id":"W4378527903","doi":"10.1016/j.msea.2023.145196","title":"Pre-deformation aging strengthening mechanism of Cu–15Ni–8Sn alloys prepared by laser additive manufacturing","year":2023,"lang":"en","type":"article","venue":"Materials Science and Engineering A","topic":"Additive Manufacturing Materials and Processes","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":false,"ca_institutions":"McGill University","funders":"National Key Research and Development Program of China; National Natural Science Foundation of China","keywords":"Materials science; Nucleation; Deformation (meteorology); Dislocation; Elongation; Composite material; Microstructure; Deformation mechanism; Grain boundary; Spinodal; Phase (matter); Metallurgy; Crystallography; Ultimate tensile strength; Thermodynamics; Chemistry","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":[0.000215158,0.0001603035,0.0001762621,0.0003557154,0.0002729269,0.0001539813,0.0003334529,0.0002394927,0.001538339],"category_scores_gemma":[0.0001851811,0.0001761548,0.0001933838,0.0001799985,0.0002609011,0.0002136544,0.0001117512,0.0002013631,0.0001598881],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002729409,"about_ca_system_score_gemma":0.0002969237,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001776502,"about_ca_topic_score_gemma":0.003319382,"domain_scores_codex":[0.9998926,0.000009239836,0.000007924057,0.00001955089,0.00004711991,0.00002364169],"domain_scores_gemma":[0.9998986,0.00001321883,0.00003087996,0.00001187573,0.00003680691,0.000008620636],"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":[0.0003136637,0.00002305209,0.001183696,0.0001113055,0.00001266148,0.0001146569,0.0001433869,0.0009228123,0.9933827,0.0005298709,0.0001066323,0.0031555],"study_design_scores_gemma":[0.000009205446,0.0002965834,0.01277683,0.000007422208,0.00002544761,0.0000881148,0.0001109698,0.004093722,0.9812058,0.00005633682,0.001318414,0.00001106642],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9985192,0.0003628288,0.0003176451,0.00001213467,0.0000137068,0.000005483408,0.0000279845,0.00001172504,0.0007292267],"genre_scores_gemma":[0.9988366,0.00007824779,0.0002785094,0.000004498612,0.000002089768,0.000002894755,0.00002463465,0.000002518499,0.0007700426],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001776502,"threshold_uncertainty_score":0.005146265,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006128655841192892,"score_gpt":0.1982940595020808,"score_spread":0.1921654036608879,"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."}}