{"id":"W4322626053","doi":"10.1007/s13632-023-00939-1","title":"Effects of Fe-Addition as a Beneficial Modifying Element on the Microstructure and Mechanical Properties of an Al–Si–Cu–Mg–Ni–Mn Piston Alloy","year":2023,"lang":"en","type":"article","venue":"Metallography Microstructure and Analysis","topic":"Aluminum Alloy Microstructure Properties","field":"Engineering","cited_by":7,"is_retracted":false,"has_abstract":false,"ca_institutions":"Université de Sherbrooke","funders":"","keywords":"Microstructure; Materials science; Alloy; Intermetallic; Scanning electron microscope; Metallurgy; Differential scanning calorimetry; Piston (optics); Transmission electron microscopy; Ultimate tensile strength; 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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0003148468,0.0005673072,0.0009410636,0.0008745777,0.000271839,0.0001105807,0.0003571999,0.0002728159,0.00004482384],"category_scores_gemma":[0.00005220444,0.0003803595,0.0004571536,0.00169954,0.0004400762,0.0001793448,0.000140517,0.0003956057,0.000001304967],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002390531,"about_ca_system_score_gemma":0.00002006175,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001700967,"about_ca_topic_score_gemma":0.0001856143,"domain_scores_codex":[0.9976842,0.0001622346,0.0006470837,0.0006066893,0.0004167352,0.0004830938],"domain_scores_gemma":[0.9989545,0.00009110253,0.0002197635,0.0004871439,0.0001235339,0.0001239492],"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":[0.0001259948,0.00002130246,0.0003186711,0.0004498879,0.002218693,0.000007812097,0.001097266,0.0004679034,0.9935769,0.0003290464,0.0001019919,0.001284463],"study_design_scores_gemma":[0.0008997154,0.0004491339,0.02058582,0.0001623709,0.003288737,0.000034072,0.001052272,0.009066348,0.9614041,0.001720517,0.0005852607,0.0007516053],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9958812,0.002843317,0.00007171296,0.000121271,0.0001789871,0.0004969161,0.0002309705,0.0001577101,0.00001793617],"genre_scores_gemma":[0.9979946,0.0006414301,0.0007993515,0.0002039295,0.00005383097,0.00004130128,0.0001770859,0.00006870311,0.00001978942],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03217282,"threshold_uncertainty_score":0.9998648,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007832329445545318,"score_gpt":0.1998108360919808,"score_spread":0.1919785066464355,"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."}}