{"id":"W4415038014","doi":"10.1016/j.cemconcomp.2025.106363","title":"A transformer-based machine learning model for optimizing the design of cementitious mixtures with mine tailings as supplementary cementitious materials","year":2025,"lang":"en","type":"article","venue":"Cement and Concrete Composites","topic":"Mineral Processing and Grinding","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Cementitious; Reuse; Tailings; Portland cement; Sorting; Compressive strength; Boosting (machine learning); Gradient boosting","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000222033,0.000208519,0.000248266,0.0001054412,0.0002640386,0.0001027414,0.0001217302,0.00003710375,0.00006648336],"category_scores_gemma":[0.000005129597,0.0001547794,0.00003996506,0.00009790943,0.00006457518,0.00006967092,0.00001612594,0.00008239243,2.91734e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002403896,"about_ca_system_score_gemma":0.00002592946,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006551576,"about_ca_topic_score_gemma":0.000008199278,"domain_scores_codex":[0.9991232,0.00002738707,0.0002968407,0.0001787824,0.00012406,0.0002497196],"domain_scores_gemma":[0.9996426,0.0001150735,0.00006102872,0.00009636426,0.00005236734,0.00003258073],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000216804,0.000005757943,0.0001010418,0.0004976879,0.0001706745,9.00508e-7,0.0003242518,0.2948932,0.7031285,0.0002352635,0.0002488594,0.0001770938],"study_design_scores_gemma":[0.001353195,0.0002162535,9.347841e-7,0.0001895114,0.0001704198,0.000001153607,0.00008469102,0.5760704,0.4213691,0.0001192859,0.0002823466,0.0001427747],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5536005,0.00156393,0.4433281,0.0002595834,0.00006499009,0.000715218,0.00008690216,0.00007476693,0.0003060067],"genre_scores_gemma":[0.9840702,0.0001149736,0.01489411,0.0001926783,0.00002028076,0.0001336948,0.000291807,0.00002379589,0.0002584996],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4304697,"threshold_uncertainty_score":0.6311722,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01125969105890825,"score_gpt":0.2189534553461743,"score_spread":0.207693764287266,"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."}}