{"id":"W1600872332","doi":"","title":"Modelling of mechanical properties of Al-Si-Cu cast alloys using the neural network","year":2007,"lang":"en","type":"article","venue":"Journal of Achievements of Materials and Manufacturing Engineering","topic":"Aluminum Alloy Microstructure Properties","field":"Engineering","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Materials science; Indentation hardness; Artificial neural network; Casting; Vickers hardness test; Metallurgy; Universal testing machine; Ceramic; Rockwell scale; Mechanical engineering; Composite material; Ultimate tensile strength; Computer science; Engineering; Microstructure; Machine learning","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008776355,0.0002138827,0.0005481601,0.0001200014,0.00003979773,0.00002544922,0.0002340665,0.00006421982,0.000005617326],"category_scores_gemma":[0.00001251742,0.0001462736,0.00008539388,0.00004501562,0.00004895223,0.0001927988,0.0001060556,0.0001641866,5.260309e-8],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000038976,"about_ca_system_score_gemma":0.0000134868,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006274162,"about_ca_topic_score_gemma":0.000002267734,"domain_scores_codex":[0.9982656,0.00002132242,0.001055395,0.00009004422,0.0002746131,0.000292982],"domain_scores_gemma":[0.9992715,0.00003145772,0.0004241778,0.0001487604,0.00006582111,0.00005825433],"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":[0.00004598123,0.000006458444,0.000009623597,0.0002965883,0.0001100994,0.000001804685,0.0001871741,0.496033,0.5031374,0.00001068889,0.000005359771,0.0001558589],"study_design_scores_gemma":[0.0003010574,0.0000889923,0.0002264144,0.0005210544,0.0000494485,0.00006161493,0.0001007076,0.03089029,0.9675393,0.00001741582,0.00007367109,0.0001300354],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9761375,0.001398296,0.02160472,0.0000185502,0.0006827555,0.0001324886,0.000006230855,0.00001673233,0.00000273695],"genre_scores_gemma":[0.9890512,0.0001628942,0.01057744,0.00001244098,0.0001490273,5.810398e-7,5.611121e-7,0.00004370806,0.000002121765],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4651427,"threshold_uncertainty_score":0.5964865,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02172261978960057,"score_gpt":0.203758014469157,"score_spread":0.1820353946795564,"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."}}