{"id":"W3094015353","doi":"10.3390/ma13214757","title":"Predicting Ultra-High-Performance Concrete Compressive Strength Using Tabular Generative Adversarial Networks","year":2020,"lang":"en","type":"article","venue":"Materials","topic":"Innovative concrete reinforcement materials","field":"Engineering","cited_by":172,"is_retracted":false,"has_abstract":true,"ca_institutions":"Western University","funders":"","keywords":"Computer science; Generative grammar; Machine learning; Artificial intelligence; Parametric statistics; Experimental data; Random forest; Nonlinear system; Boosting (machine learning); Predictive modelling; Performance prediction; Simulation; Mathematics","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0002408299,0.0003966197,0.000621415,0.00005712247,0.0001579707,0.0002037247,0.0002582537,0.0001699208,0.0009099771],"category_scores_gemma":[0.00006779429,0.0004001818,0.00003773217,0.0002143355,0.00007628356,0.0004270851,0.0000852376,0.0001475147,0.00004652926],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001045963,"about_ca_system_score_gemma":0.00002948901,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003548589,"about_ca_topic_score_gemma":2.368232e-7,"domain_scores_codex":[0.9980584,0.0001146128,0.0007334886,0.000316678,0.0002446417,0.0005321975],"domain_scores_gemma":[0.9992806,0.00005401735,0.0002132709,0.0002134215,0.0001221251,0.0001166047],"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":[0.00007700648,5.792797e-7,0.0001134133,0.0001235431,0.0001262367,0.00000941685,0.0003211875,0.1862997,0.8124707,0.0002386961,0.0001774308,0.00004204527],"study_design_scores_gemma":[0.0008096365,0.00007843248,0.00005848228,0.00008292799,0.00004225812,0.000002442605,0.00005893393,0.2021588,0.795954,0.000002615845,0.0004045173,0.0003470292],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.982932,0.00005553849,0.01264244,0.00002110375,0.002515501,0.0005500943,0.0002377255,0.0004845667,0.0005609758],"genre_scores_gemma":[0.9954295,0.00004978023,0.001612217,0.0002557276,0.002289282,0.0000354806,0.0002286265,0.00009064477,0.000008767896],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01651674,"threshold_uncertainty_score":0.999845,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01613690657784114,"score_gpt":0.209359993416318,"score_spread":0.1932230868384769,"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."}}