{"id":"W1989439005","doi":"10.1016/j.jallcom.2014.03.043","title":"Predicting the flow stress of high pressure die cast magnesium alloys","year":2014,"lang":"en","type":"article","venue":"Journal of Alloys and Compounds","topic":"Aluminum Alloys Composites Properties","field":"Engineering","cited_by":22,"is_retracted":false,"has_abstract":false,"ca_institutions":"Western University","funders":"AUTO21 Network of Centres of Excellence; Networks of Centres of Excellence of Canada; Natural Resources Canada","keywords":"Materials science; Flow stress; Magnesium; Grain size; Metallurgy; Casting; Die casting; Stress (linguistics); Ultimate tensile strength; Die (integrated circuit); Magnesium alloy; Yield (engineering); Indentation; Plasticity; Flow (mathematics); Tensile testing; Composite material; Microstructure; Geometry; Mathematics","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":[0.000178464,0.0005868264,0.0003604361,0.0006657033,0.0003538811,0.0005311369,0.0004368444,0.0007565294,0.001210288],"category_scores_gemma":[0.0004810019,0.0003904466,0.0003172268,0.0002986154,0.0002855046,0.0004245915,0.000192093,0.0004102454,0.0002391171],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005430425,"about_ca_system_score_gemma":0.0005110472,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006652995,"about_ca_topic_score_gemma":0.008042563,"domain_scores_codex":[0.9999254,0.000005933093,0.000002956928,0.00001157761,0.00004151808,0.00001262329],"domain_scores_gemma":[0.9998019,0.00008703094,0.0000225865,0.00001329299,0.00005903879,0.0000161461],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.001240128,0.0004447283,0.06463417,0.0002053081,0.0000762964,0.0005003742,0.0001628868,0.579285,0.3004862,0.001244042,0.0009911035,0.0507297],"study_design_scores_gemma":[0.00001951842,0.0002459796,0.02708534,0.000006404921,0.00001495867,0.00003838718,0.0000494552,0.911585,0.06037681,0.0001885515,0.0003745933,0.00001498106],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.994568,0.00009811777,0.003684544,0.00003643781,0.00001674796,0.00001292044,0.0001365745,0.0002127611,0.001233945],"genre_scores_gemma":[0.9986917,0.00004436851,0.0007519994,0.000002690916,0.00000433758,0.000003486803,0.00007512618,0.00001164506,0.0004146208],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.006652995,"threshold_uncertainty_score":0.01322854,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006004471973158661,"score_gpt":0.1767279473313671,"score_spread":0.1707234753582084,"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."}}