{"id":"W2330514267","doi":"10.1007/s00339-016-9840-1","title":"Optimization of the EMS process parameters in compocasting of high-wear-resistant Al-nano-TiC composites","year":2016,"lang":"en","type":"article","venue":"Applied Physics A","topic":"Aluminum Alloys Composites Properties","field":"Engineering","cited_by":53,"is_retracted":false,"has_abstract":false,"ca_institutions":"McMaster University","funders":"","keywords":"Materials science; Particle swarm optimization; Composite material; Tribology; Nanocomposite; Adaptive neuro fuzzy inference system; Nano-; Particle (ecology); Fuzzy logic; Computer science; Algorithm; Fuzzy control system","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":[],"consensus_categories":[],"category_scores_codex":[0.00006205949,0.0001419643,0.0002432235,0.0000340778,0.00002708355,0.000009597044,0.0002416401,0.00003449323,0.000002377153],"category_scores_gemma":[0.000007558315,0.0000931934,0.00004066087,0.0002729886,0.0001146365,0.0000708746,0.0000563859,0.00006969534,0.000002594169],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004391226,"about_ca_system_score_gemma":0.00001389,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000251013,"about_ca_topic_score_gemma":0.000003551341,"domain_scores_codex":[0.9991807,0.00001624025,0.0003041989,0.0001367137,0.0001891355,0.0001730133],"domain_scores_gemma":[0.999432,0.0001355816,0.0001108023,0.0002585612,0.00004240926,0.00002067121],"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":[0.00001940352,0.00002796131,0.0003971221,0.00007881565,0.00002286269,1.172684e-7,0.0002855735,0.5642588,0.4335783,0.0008586725,0.000007607168,0.0004648593],"study_design_scores_gemma":[0.0006043861,0.00002744198,0.00142195,0.0003376871,0.00002551783,5.236174e-7,0.00007151726,0.1497568,0.846229,0.00131652,0.000005119829,0.0002034856],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9802678,0.00003573396,0.01888447,0.00002849474,0.00006297291,0.0002679187,0.000008966098,0.0000511916,0.000392442],"genre_scores_gemma":[0.9967325,0.000005039386,0.003160008,0.00001517608,0.00001236063,0.00003185957,0.000003168279,0.00003514969,0.000004742419],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4145019,"threshold_uncertainty_score":0.3800316,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009395983898828475,"score_gpt":0.1866584605314037,"score_spread":0.1772624766325752,"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."}}