{"id":"W2787153607","doi":"10.1007/978-3-319-72526-0_72","title":"Development of Novel Squeeze Cast High Tensile Strength Al–Si–Cu–Ni–Sr Alloys","year":2018,"lang":"en","type":"book-chapter","venue":"The minerals, metals & materials series","topic":"Aluminum Alloy Microstructure Properties","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Windsor","funders":"Natural Sciences and Engineering Research Council of Canada; University of Windsor; Ford Motor Company","keywords":"Ultimate tensile strength; Materials science; Taguchi methods; Alloy; Elongation; Aluminium; Metallurgy; Yield (engineering); Composite material","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0002078262,0.0004055306,0.0003090188,0.0003527736,0.0002084693,0.0006275367,0.0007497465,0.0003615927,0.002367707],"category_scores_gemma":[0.0001635898,0.0003768862,0.0003640678,0.000289517,0.0001802035,0.000691298,0.0004007243,0.000651397,0.001993966],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003221698,"about_ca_system_score_gemma":0.0003512921,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004982015,"about_ca_topic_score_gemma":0.002118138,"domain_scores_codex":[0.9998204,0.000006192943,0.000008902474,0.0000264675,0.0001208682,0.00001718203],"domain_scores_gemma":[0.9999149,0.000003901942,0.00001994609,0.00001183089,0.00003537614,0.00001404058],"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":[0.00005288485,0.00004478099,0.0001621507,0.0003761027,0.00002292305,0.0002014039,0.00007652315,0.0006479485,0.9720263,0.00305442,0.001538177,0.02179637],"study_design_scores_gemma":[0.00003261152,0.0003819823,0.001897007,0.00003169444,0.00004513064,0.0006865067,0.00008974804,0.005868862,0.9014763,0.0004585571,0.08900745,0.00002415333],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7399736,0.02031468,0.07900453,0.0006279196,0.002205211,0.0003984338,0.000936158,0.002242121,0.1542974],"genre_scores_gemma":[0.754932,0.006737672,0.1115563,0.0001441275,0.0002087635,0.0001452905,0.001161247,0.0004695471,0.124645],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002367707,"threshold_uncertainty_score":0.007920742,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01920342853112172,"score_gpt":0.2045172882745434,"score_spread":0.1853138597434216,"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."}}