{"id":"W4388423449","doi":"10.1007/s10163-023-01834-1","title":"Incorporating coarse and fine recycled aggregates into concrete mixes: mechanical characterization and environmental impact","year":2023,"lang":"en","type":"article","venue":"Journal of Material Cycles and Waste Management","topic":"Recycled Aggregate Concrete Performance","field":"Engineering","cited_by":32,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"Science and Technology Development Fund; Arab Academy for Science, Technology and Maritime Transport","keywords":"Demolition waste; Demolition; Aggregate (composite); Ultimate tensile strength; Life-cycle assessment; Raw material; Construction waste; Environmental science; Cement; Compressive strength; Waste management; Materials science; Civil engineering; Engineering; Composite material; Production (economics)","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":[0.0003571393,0.0003004141,0.0001717498,0.0005626412,0.0001615179,0.0003244059,0.0001276708,0.0002315038,0.0004620666],"category_scores_gemma":[0.0002280285,0.0001280532,0.0002245206,0.0003168396,0.0002333994,0.0001565916,0.0001862236,0.0002163779,0.0001013981],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002272261,"about_ca_system_score_gemma":0.0001757248,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00175692,"about_ca_topic_score_gemma":0.004282013,"domain_scores_codex":[0.9996879,0.00004365359,0.00002868926,0.00005007836,0.0001518534,0.00003787156],"domain_scores_gemma":[0.9998254,0.00002462269,0.00005062009,0.00001644627,0.00006051969,0.0000223241],"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":[0.00009369355,0.0000399027,0.005614447,0.00002871177,0.0000126612,0.0000413743,0.00003626612,0.001044129,0.9909395,0.00002661309,0.000009856381,0.002112907],"study_design_scores_gemma":[0.000004884351,0.0006976648,0.04692468,0.000008285725,0.00003997629,0.00007077346,0.0001437938,0.003287504,0.9483181,0.00003325758,0.0004590807,0.00001193751],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9994468,0.00005903943,0.0002798642,0.000001786368,9.668519e-7,0.000005725145,0.00006091529,0.000004120652,0.0001408376],"genre_scores_gemma":[0.9989318,0.00006981706,0.0006288885,0.000002987041,6.462572e-7,0.000006899349,0.00006339467,0.000002210833,0.0002932534],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.00175692,"threshold_uncertainty_score":0.003493369,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.005484930110668326,"score_gpt":0.2001905217670912,"score_spread":0.1947055916564229,"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."}}