{"id":"W2155456746","doi":"10.1504/ijcmsse.2007.014874","title":"Applications of artificial intelligence methods for modelling of solidus temperature for hypoeutectic Al-Si-Cu alloys","year":2007,"lang":"en","type":"article","venue":"International Journal of Computational Materials Science and Surface Engineering","topic":"Aluminum Alloy Microstructure Properties","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"University of Windsor","keywords":"Solidus; Eutectic system; Materials science; Metallurgy; Casting; Atmospheric temperature range; Aluminium; Thermodynamics; Alloy; Physics","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001815097,0.0001184369,0.0002408968,0.0002726746,0.00004733735,0.00006962704,0.0003944579,0.00004694429,0.000003402757],"category_scores_gemma":[0.0001700947,0.0001106259,0.00005527355,0.0001804356,0.0001217831,0.0002862786,0.00003845533,0.00007153943,1.458373e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007391982,"about_ca_system_score_gemma":0.00009965259,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00000298117,"about_ca_topic_score_gemma":3.854391e-7,"domain_scores_codex":[0.9986452,0.000008091055,0.0007116141,0.0001188016,0.0003539793,0.0001622579],"domain_scores_gemma":[0.9976751,0.0004287086,0.0002054982,0.0000597147,0.001575946,0.00005509831],"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":[0.0000337556,0.000006519789,0.000002575118,0.00004555858,0.00003081319,1.879679e-7,0.0001431476,0.5214564,0.4735824,0.001512798,0.00000315037,0.003182733],"study_design_scores_gemma":[0.00008789593,0.00004143594,0.00003877622,0.0000543487,0.00001220022,0.00003322076,0.00007529988,0.3998001,0.5949727,0.004619027,0.000180721,0.00008433626],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3963216,0.0001918366,0.6027016,0.00004343545,0.0005675495,0.0001364224,0.00002558237,0.000009629487,0.000002313354],"genre_scores_gemma":[0.609668,0.00001800337,0.3902032,0.000009980336,0.00008282487,0.000003626201,0.000002624415,0.00001077413,9.17326e-7],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.2133464,"threshold_uncertainty_score":0.4511192,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.023079993721491,"score_gpt":0.3060654186923217,"score_spread":0.2829854249708307,"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."}}