{"id":"W3090938765","doi":"10.3390/ma13194331","title":"Mixture Optimization of Recycled Aggregate Concrete Using Hybrid Machine Learning Model","year":2020,"lang":"en","type":"article","venue":"Materials","topic":"Recycled Aggregate Concrete Performance","field":"Engineering","cited_by":104,"is_retracted":false,"has_abstract":true,"ca_institutions":"Western University","funders":"","keywords":"Demolition waste; Compressive strength; Boosting (machine learning); Aggregate (composite); Carbon footprint; Gradient boosting; Particle swarm optimization; Computer science; Robustness (evolution); Random forest; Machine learning; Environmental science; Materials science; Demolition; Engineering; Greenhouse gas; Composite material; Civil engineering; Geology","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":[0.0006320779,0.0007670442,0.0009293712,0.0004597236,0.000228954,0.0007438812,0.0007046188,0.0009977478,0.0009905504],"category_scores_gemma":[0.0008790604,0.0004619102,0.0009263027,0.0003894008,0.0004273482,0.0005500554,0.0005332723,0.0007622754,0.000224858],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005581098,"about_ca_system_score_gemma":0.0007724995,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008615181,"about_ca_topic_score_gemma":0.007007706,"domain_scores_codex":[0.9997435,0.00007110726,0.00001346027,0.00007137093,0.00006348903,0.0000369605],"domain_scores_gemma":[0.9996428,0.0002015924,0.00004522528,0.00001420667,0.0000820986,0.00001395417],"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":[0.00001091289,0.000007489368,0.000184868,0.00001109629,0.00001011058,0.000009813299,0.000005221156,0.9954658,0.0003869988,0.0004837536,0.00006240042,0.003361599],"study_design_scores_gemma":[7.391777e-7,0.000003323977,0.00002434736,8.271547e-7,0.000001578977,7.947132e-7,4.853951e-7,0.9997461,0.00008136749,0.0001054218,0.00003413205,8.748167e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.09017824,0.0008678191,0.9024414,0.0002074688,0.00005393391,0.00004075332,0.0001118145,0.0005468294,0.005551813],"genre_scores_gemma":[0.9499025,0.0002844937,0.04514309,0.00007899533,0.00001946598,0.0001167362,0.0001714674,0.00004636187,0.004236895],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.008615181,"threshold_uncertainty_score":0.01713008,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01804530119336354,"score_gpt":0.2086156802450093,"score_spread":0.1905703790516458,"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."}}