{"id":"W4230571877","doi":"10.32920/ryerson.14654961.v1","title":"Development of statistical models to simulate and optimize self-consolidating concrete mixes incorporating high volumes of fly ash","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Innovations in Concrete and Construction Materials","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Fly ash; Superplasticizer; Materials science; Self-consolidating concrete; Slump; Rheology; Mortar; Cement; Viscosity; Composite material; Flow (mathematics); Geotechnical engineering; Environmental science; Mathematics; Engineering; Compressive strength","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":[0.0008393243,0.0008522267,0.0007712527,0.0006109978,0.00035132,0.0007415437,0.001049521,0.0009761908,0.0009302283],"category_scores_gemma":[0.001976979,0.0006579276,0.001066281,0.0005001022,0.0003330577,0.0005598849,0.0003784908,0.0009925417,0.0002148523],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001021792,"about_ca_system_score_gemma":0.001919535,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02510102,"about_ca_topic_score_gemma":0.0177022,"domain_scores_codex":[0.9997523,0.00005828978,0.0000175405,0.00004769785,0.00007839889,0.00004568734],"domain_scores_gemma":[0.9987025,0.0007910511,0.0001704473,0.00004171787,0.000249696,0.00004455905],"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":[0.000004957298,0.0000159179,0.000352164,0.000007493318,0.000006234756,0.000005013605,0.000003917859,0.9976677,0.0002668334,0.0003039366,0.00003184373,0.001333891],"study_design_scores_gemma":[0.000001047843,0.000006453171,0.00005547695,6.601402e-7,0.000001865819,7.541777e-7,0.000001160247,0.9996572,0.0001743552,0.0000603805,0.0000394804,0.000001245625],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2915339,0.0004418546,0.6992347,0.0002780143,0.00007029682,0.0002041717,0.0006067572,0.001299161,0.006331123],"genre_scores_gemma":[0.9164356,0.0002874081,0.07979657,0.0000422778,0.00001785452,0.0003695719,0.0004756724,0.0001017307,0.002473384],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.02510102,"threshold_uncertainty_score":0.04990983,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01933923760485793,"score_gpt":0.2371389828570067,"score_spread":0.2177997452521488,"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."}}