{"id":"W4292574950","doi":"10.3390/su141610373","title":"Indirect Analysis of Concrete Slump Using Different Metaheuristic-Empowered Neural Processors","year":2022,"lang":"en","type":"article","venue":"Sustainability","topic":"Infrastructure Maintenance and Monitoring","field":"Engineering","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University","funders":"","keywords":"Superplasticizer; Slump; Metaheuristic; Aggregate (composite); Artificial neural network; Sensitivity (control systems); Extreme learning machine; Concrete slump test; Computer science; Engineering; Algorithm; Machine learning; Cement; Materials science; Composite material","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":[0.0005558857,0.0007861272,0.0005191151,0.0006318237,0.0002317839,0.0005746,0.0006775516,0.0008440643,0.0006239036],"category_scores_gemma":[0.001197794,0.000353442,0.0007615617,0.000450263,0.0003558655,0.0009081357,0.0005719501,0.0006587348,0.0001120204],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004443721,"about_ca_system_score_gemma":0.0006212916,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003233783,"about_ca_topic_score_gemma":0.004001023,"domain_scores_codex":[0.9998455,0.00004033181,0.000008877738,0.00003322911,0.0000468116,0.00002520737],"domain_scores_gemma":[0.9996754,0.0001541597,0.00006417935,0.00002643072,0.00006363643,0.00001612983],"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":[0.00002603664,0.00002688463,0.001096122,0.00002506067,0.00002790398,0.00002262892,0.00001489515,0.977986,0.002655551,0.0008251165,0.00006466644,0.01722902],"study_design_scores_gemma":[6.435411e-7,0.000007032674,0.0000916053,0.000001163894,0.00000259418,0.000002177636,0.000001975927,0.999366,0.0003278044,0.0001726374,0.00002535289,0.000001149672],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1463556,0.0002874315,0.8505325,0.0001095036,0.0000301881,0.0000351109,0.00004795637,0.0003214081,0.002280341],"genre_scores_gemma":[0.9243829,0.0001940908,0.07419594,0.00003513721,0.00001482597,0.00006340956,0.00007499212,0.0000281036,0.001010464],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003233783,"threshold_uncertainty_score":0.006429911,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008219701447709319,"score_gpt":0.2438866088190427,"score_spread":0.2356669073713334,"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."}}