{"id":"W4386229497","doi":"10.31399/asm.amp.2016-03.p016","title":"High-Temperature Aluminum Alloys for Automotive Powertrains","year":2016,"lang":"en","type":"article","venue":"AM&P Technical Articles","topic":"Material Properties and Applications","field":"Materials Science","cited_by":15,"is_retracted":false,"has_abstract":true,"ca_institutions":"Canadian Nuclear Laboratories; Natural Resources Canada","funders":"","keywords":"Materials science; Alloy; Automotive industry; Aluminium; Metallurgy; Powertrain; Ultimate tensile strength; Engineering","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":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0002444971,0.0001327461,0.0001741082,0.00002172081,0.0001696105,0.00007719928,0.0002966402,0.00009777704,0.0009191806],"category_scores_gemma":[0.0002029081,0.00007521651,0.00006879848,0.00008625443,0.0002153627,0.0001633721,0.0001081022,0.00004122725,0.0004500859],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003616219,"about_ca_system_score_gemma":0.00002435734,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003366092,"about_ca_topic_score_gemma":0.00002460477,"domain_scores_codex":[0.9988682,0.00003120782,0.0002640739,0.0003424413,0.0001447449,0.0003492867],"domain_scores_gemma":[0.9992486,0.000123264,0.00005901289,0.000352114,0.0001080469,0.0001089375],"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":[0.00003763956,0.00007586074,0.00001389268,0.000006820576,0.000003060277,6.093193e-7,0.00002346652,0.000001153577,0.9653199,0.02388719,0.008613463,0.002016977],"study_design_scores_gemma":[0.0003851894,0.0001493411,0.0009546714,0.00003017835,0.00001134567,0.000005123914,0.00002561588,0.000006203668,0.9702748,0.01264528,0.01535015,0.0001620762],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9832185,0.00001898804,0.001578758,0.01379598,0.0002210976,0.0004538423,0.0001301129,0.0004198202,0.0001629105],"genre_scores_gemma":[0.990828,0.000008096853,0.00735897,0.0006254885,0.0002226113,0.0002558003,0.0000039727,0.00002141373,0.0006756574],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0131705,"threshold_uncertainty_score":0.9999941,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01484705326416636,"score_gpt":0.2451948142372048,"score_spread":0.2303477609730384,"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."}}