{"id":"W4414052478","doi":"10.1016/j.conbuildmat.2025.143369","title":"Hybrid generative adversarial network and machine learning approach for performance prediction of marshall stability and marshall flow of recycled asphalt shingle pavements","year":2025,"lang":"en","type":"article","venue":"Construction and Building Materials","topic":"Infrastructure Maintenance and Monitoring","field":"Engineering","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Guelph; McMaster University","funders":"","keywords":"Asphalt; Gradation; Stability (learning theory); Artificial neural network; Performance prediction; Generalization; Key (lock); Generative grammar","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001244519,0.001032837,0.0006883348,0.0006912549,0.0002203131,0.0006299629,0.0008751279,0.0008032913,0.001048478],"category_scores_gemma":[0.002016594,0.0003349929,0.0007835242,0.0003834692,0.0006455733,0.0005550957,0.0007210308,0.001250936,0.0001887525],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007825796,"about_ca_system_score_gemma":0.0005362503,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007346049,"about_ca_topic_score_gemma":0.004145566,"domain_scores_codex":[0.9995916,0.0001587039,0.0000155975,0.0001044809,0.00006624019,0.0000633545],"domain_scores_gemma":[0.9990864,0.0006148004,0.00009249699,0.00004644317,0.0001212777,0.00003853867],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00003091194,0.00001729723,0.0008469354,0.00001109356,0.00001556893,0.0000274595,0.00001171805,0.9917883,0.0004377878,0.0005880694,0.0001552178,0.006069679],"study_design_scores_gemma":[6.565657e-7,0.000004972472,0.00009906164,0.000001137056,0.000001862821,0.000002162942,0.000001529977,0.9994749,0.0001468665,0.0002417944,0.00002330003,0.000001712062],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3113981,0.000912544,0.681359,0.0006067672,0.0001061161,0.00008244034,0.0003711066,0.0009377704,0.004226137],"genre_scores_gemma":[0.9838687,0.000140041,0.01400502,0.00007989927,0.00001976718,0.00005356724,0.0002701069,0.00002536223,0.001537489],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.007346049,"threshold_uncertainty_score":0.01460654,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01162721071899831,"score_gpt":0.2056442364232142,"score_spread":0.1940170257042159,"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."}}