{"id":"W4387425537","doi":"10.1016/j.conbuildmat.2023.133560","title":"Prediction of self-consolidating concrete properties using XGBoost machine learning algorithm: Part 1–Workability","year":2023,"lang":"en","type":"article","venue":"Construction and Building Materials","topic":"Infrastructure Maintenance and Monitoring","field":"Engineering","cited_by":44,"is_retracted":false,"has_abstract":false,"ca_institutions":"Concordia University","funders":"","keywords":"Funnel; Self-consolidating concrete; Slump; Concrete slump test; Funnel plot; Range (aeronautics); Materials science; Mathematics; Algorithm; Geotechnical engineering; Machine learning; Artificial intelligence; Composite material; Statistics; Engineering; Computer science; Mechanical 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003793715,0.0001561078,0.0002612376,0.0001233286,0.0001800848,0.00007086341,0.00004935489,0.00009303256,0.00003463546],"category_scores_gemma":[0.00006446875,0.0001451908,0.00002868783,0.0001766651,0.0001043905,0.0001935605,0.00005022078,0.0001169545,0.000001875406],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004705752,"about_ca_system_score_gemma":0.00001531122,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002666193,"about_ca_topic_score_gemma":2.199317e-7,"domain_scores_codex":[0.9990291,0.00005527058,0.0003896524,0.0001806292,0.0001081481,0.0002371767],"domain_scores_gemma":[0.99966,0.00002753353,0.00009934639,0.0001037177,0.0000663941,0.00004302027],"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":[0.00000760381,6.587169e-7,0.004322269,0.0002457978,0.00003993283,0.000001181386,0.0002474261,0.001839879,0.9839515,0.00008260433,0.00001160539,0.00924951],"study_design_scores_gemma":[0.0002165381,0.00001911687,0.0006109957,0.0002573552,0.00003563886,0.00006292286,0.0003791075,0.03710449,0.9600446,0.00009749788,0.001022465,0.0001493141],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9941667,0.00007684997,0.002376251,0.000005551479,0.002405749,0.0001515791,0.00006184052,0.0006934626,0.00006196672],"genre_scores_gemma":[0.9847288,0.0001724875,0.01464423,0.000002947599,0.0003880631,0.00001032714,0.00001911705,0.00002701646,0.000006984579],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03526461,"threshold_uncertainty_score":0.5920709,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02139890289777721,"score_gpt":0.2131587495946191,"score_spread":0.1917598466968418,"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."}}