{"id":"W4224296554","doi":"10.3390/su14094934","title":"Novel Evolutionary-Optimized Neural Network for Predicting Fresh Concrete Slump","year":2022,"lang":"en","type":"article","venue":"Sustainability","topic":"Infrastructure Maintenance and Monitoring","field":"Engineering","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University","funders":"","keywords":"Artificial neural network; Slump; Perceptron; Computer science; Data mining; Metaheuristic; Multilayer perceptron; Sensitivity (control systems); Evolutionary algorithm; Artificial intelligence; Mathematical optimization; Algorithm; Machine learning; Engineering; Mathematics; Materials science","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":[0.0004592619,0.0001718046,0.0002150162,0.00003996828,0.0005269117,0.00002334149,0.0002006722,0.00005321059,0.00007369184],"category_scores_gemma":[0.0003354454,0.0001887092,0.0001320819,0.0002057446,0.0000569832,0.000122286,0.0001475071,0.0003485836,2.970027e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001327768,"about_ca_system_score_gemma":0.0001162272,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006090389,"about_ca_topic_score_gemma":0.000002995368,"domain_scores_codex":[0.9986003,0.00003898989,0.0002768499,0.00026103,0.0001810415,0.0006417803],"domain_scores_gemma":[0.9991777,0.0001582592,0.00004474596,0.0002931471,0.0002555368,0.00007061588],"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":[0.0001113382,0.000003914129,0.007561914,0.0001441895,0.00003026111,0.000003631052,0.0002950756,0.9836729,0.0002450265,0.001447654,0.005656767,0.000827304],"study_design_scores_gemma":[0.002057393,0.0001723472,0.01506201,0.000009862142,0.0000465999,0.00002835689,0.002597479,0.9205732,0.0001612264,0.01039308,0.04840561,0.0004928457],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9137452,0.0005807607,0.07661144,0.0004121005,0.004760192,0.001948925,0.0002176807,0.001003882,0.000719818],"genre_scores_gemma":[0.9876961,0.000001431631,0.0107414,0.00005344291,0.0007948044,0.0005155987,0.00004332435,0.00003971282,0.0001141663],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.07395091,"threshold_uncertainty_score":0.7695339,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.005810895286908011,"score_gpt":0.2189142573738164,"score_spread":0.2131033620869084,"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."}}