{"id":"W3092657053","doi":"10.1016/j.conbuildmat.2020.121117","title":"Machine learning-based prediction for compressive and flexural strengths of steel fiber-reinforced concrete","year":2020,"lang":"en","type":"article","venue":"Construction and Building Materials","topic":"Innovative concrete reinforcement materials","field":"Engineering","cited_by":402,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Victoria","funders":"National Research Foundation of Korea","keywords":"Flexural strength; Compressive strength; Silica fume; Overfitting; Fiber-reinforced concrete; Materials science; Structural engineering; Random forest; Computer science; Fiber; Artificial neural network; Machine learning; Composite material; Engineering","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":[0.0007712091,0.0006657838,0.0004024772,0.0007645254,0.0002306015,0.0003635977,0.0005758879,0.000890415,0.0006820445],"category_scores_gemma":[0.002381012,0.0002733023,0.0004574605,0.0003529877,0.0003198756,0.0004983727,0.0002972052,0.0007131919,0.0002980681],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007061684,"about_ca_system_score_gemma":0.0005482589,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01022755,"about_ca_topic_score_gemma":0.01046397,"domain_scores_codex":[0.9997838,0.0000492705,0.00001529745,0.00006625721,0.00005090524,0.00003452116],"domain_scores_gemma":[0.9984584,0.0009033261,0.0001494159,0.00006702142,0.0003691082,0.00005282188],"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":[0.0001634404,0.0001726828,0.009686982,0.00002709803,0.00003517376,0.00003755342,0.00001098151,0.9399676,0.003735619,0.0002076096,0.0004534596,0.0455019],"study_design_scores_gemma":[0.000001301936,0.000007031316,0.0009270117,8.427057e-7,0.000001666641,0.000002099435,0.000001217532,0.9983235,0.0006594953,0.00006033601,0.00001393633,0.000001507254],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9086614,0.0003385985,0.08843678,0.0001844826,0.00006560032,0.00002986416,0.0003255317,0.0006100306,0.001347641],"genre_scores_gemma":[0.9937586,0.00004199535,0.005418603,0.00001240641,0.00001047271,0.00001266969,0.0002371191,0.00000864791,0.0004995978],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01022755,"threshold_uncertainty_score":0.02033603,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01244724235980938,"score_gpt":0.2253780462767145,"score_spread":0.2129308039169051,"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."}}