{"id":"W4408741581","doi":"10.1016/j.conbuildmat.2025.140927","title":"Development and fracture characterization of enhanced ductile engineered geopolymer composites utilizing construction and demolition waste-based recycled binders and aggregates","year":2025,"lang":"en","type":"article","venue":"Construction and Building Materials","topic":"Concrete and Cement Materials Research","field":"Engineering","cited_by":9,"is_retracted":false,"has_abstract":false,"ca_institutions":"Toronto Metropolitan University","funders":"Natural Sciences and Engineering Research Council of Canada; Türkiye Bilimsel ve Teknolojik Araştırma Kurumu","keywords":"Materials science; Geopolymer; Demolition waste; Demolition; Composite material; Characterization (materials science); Fracture (geology); Geopolymer cement; Compressive strength; Civil engineering; Nanotechnology","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0001909511,0.0002451137,0.0001758292,0.0003884948,0.0001599201,0.000277156,0.0001796247,0.0002694067,0.0007211826],"category_scores_gemma":[0.0001815512,0.0001638278,0.00020592,0.0002275114,0.0001934394,0.0002333758,0.0001456335,0.0003222318,0.0001385471],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002410394,"about_ca_system_score_gemma":0.0002803307,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009099885,"about_ca_topic_score_gemma":0.004232031,"domain_scores_codex":[0.9998281,0.000008369028,0.00000866699,0.00002889187,0.00009699901,0.00002887151],"domain_scores_gemma":[0.9998258,0.00002173704,0.00005466275,0.00001305541,0.00006475707,0.00002000746],"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":[0.00002617307,0.0000160715,0.0002062553,0.00001920818,0.000001752159,0.00002356278,0.00001611806,0.0003570985,0.9978259,0.00005081573,0.000009984629,0.001447078],"study_design_scores_gemma":[0.000001566976,0.00007691189,0.002157689,0.000001478239,0.00000471461,0.00002683074,0.00001618569,0.001357546,0.9960052,0.0000106849,0.0003375372,0.000003628128],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9947022,0.0001411822,0.004289947,0.000008407998,0.000005730986,0.00001071652,0.00008965265,0.00005082505,0.0007014014],"genre_scores_gemma":[0.9954181,0.0001146371,0.003154541,0.00000645351,0.000001668775,0.0000109746,0.00008334281,0.00001621882,0.001194126],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0009099885,"threshold_uncertainty_score":0.002412617,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007351666689512328,"score_gpt":0.2200827613780071,"score_spread":0.2127310946884948,"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."}}