{"id":"W2229300237","doi":"10.1061/(asce)mt.1943-5533.0001492","title":"Rational Mix-Design Procedure for Cold In-Place Recycling Asphalt Mixtures and Performance Prediction","year":2016,"lang":"en","type":"article","venue":"Journal of Materials in Civil Engineering","topic":"Asphalt Pavement Performance Evaluation","field":"Engineering","cited_by":37,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Federal Highway Administration; University of Illinois at Urbana-Champaign; State of Connecticut Department of Transportation","keywords":"Asphalt; Ultimate tensile strength; Creep; Asphalt pavement; Cracking; Aggregate (composite); Engineering; Civil engineering; Fatigue cracking; Waste management; Rut; Geotechnical engineering; Environmental science; Materials science; Composite material","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001673784,0.001234906,0.000877197,0.0009146556,0.0003936627,0.0005374098,0.0009079735,0.0004245599,0.004366051],"category_scores_gemma":[0.001284322,0.0005585571,0.0007759221,0.0004144291,0.0003092843,0.0004188845,0.0005138342,0.000735227,0.0008851679],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000769646,"about_ca_system_score_gemma":0.001454348,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002044286,"about_ca_topic_score_gemma":0.004790266,"domain_scores_codex":[0.9992995,0.0001539663,0.00003121167,0.00008762382,0.0003779976,0.000049699],"domain_scores_gemma":[0.9996746,0.0001008252,0.00005345495,0.00003135617,0.0001290167,0.00001065161],"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":[0.0001707761,0.0002559395,0.001371432,0.0003734514,0.0000593995,0.0001006446,0.0001148148,0.8073936,0.08463898,0.01008776,0.0009339988,0.09449933],"study_design_scores_gemma":[0.00004069948,0.0006442735,0.000654513,0.00001613543,0.00004536546,0.00004336827,0.00003022415,0.9531369,0.03826279,0.001624,0.005476601,0.00002516302],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02631522,0.00007822148,0.9686887,0.00001992903,0.00001358376,0.0003700514,0.0001175686,0.0005291547,0.003867602],"genre_scores_gemma":[0.3694358,0.0001511113,0.6255001,0.00003035053,0.000008443179,0.001341927,0.0002444535,0.0001449875,0.003142885],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004366051,"threshold_uncertainty_score":0.01460588,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01486937235647153,"score_gpt":0.2267700078279459,"score_spread":0.2119006354714744,"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."}}