{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001109204,0.0002113206,0.0004028972,0.00005384765,0.00004980183,0.00005119578,0.0001681583,0.00007884769,0.0002660789],"category_scores_gemma":[0.00005445586,0.0002142647,0.00004721526,0.0001341668,0.00003068883,0.0002320877,0.00005767123,0.0001091606,0.00001196908],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003962397,"about_ca_system_score_gemma":0.00001496054,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001876234,"about_ca_topic_score_gemma":1.758153e-7,"domain_scores_codex":[0.9989361,0.00005602551,0.0004311574,0.00019192,0.0001471075,0.0002376971],"domain_scores_gemma":[0.9995318,0.00001849375,0.0001522004,0.0001552964,0.00005192903,0.00009027169],"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":[0.00002739418,2.68268e-7,0.00001386245,0.0001063476,0.00001724832,0.000003682678,0.0001137165,0.5327928,0.4667256,0.00000650376,0.00003554594,0.0001570622],"study_design_scores_gemma":[0.0002200008,0.00001471111,0.000001028564,0.00005409855,0.0000162418,0.000006167695,0.000003777248,0.5629445,0.4364683,0.000008508632,0.0001359084,0.0001267668],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9200501,0.0004924272,0.07808387,0.0000676194,0.0002531345,0.0001652311,0.0001451467,0.000373562,0.0003689321],"genre_scores_gemma":[0.9842743,0.0004236081,0.01489537,0.00006280754,0.0001253052,0.000006546539,0.0001099237,0.00007826215,0.00002383212],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.06422427,"threshold_uncertainty_score":0.8737459,"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."}}