{"id":"W3216576504","doi":"10.35940/ijrte.d6631.1110421","title":"Computation of Compressive Strength of GGBS Mixed Concrete using Machine Learning","year":2021,"lang":"en","type":"article","venue":"International Journal of Recent Technology and Engineering (IJRTE)","topic":"Innovative concrete reinforcement materials","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Compressive strength; Ground granulated blast-furnace slag; Support vector machine; Artificial neural network; Cement; Computer science; Aggregate (composite); Machine learning; 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000159743,0.000117865,0.0003072626,0.0005294082,0.00001430893,0.00001183892,0.0001467381,0.0001065249,0.00004360884],"category_scores_gemma":[0.000193292,0.0001228242,0.00004275055,0.0002604297,0.00005147909,0.000116397,0.00006513874,0.0002484907,3.185656e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005599068,"about_ca_system_score_gemma":0.00002690812,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000002293473,"about_ca_topic_score_gemma":2.246491e-7,"domain_scores_codex":[0.9990394,0.00001776745,0.0005681716,0.00006965423,0.0001963527,0.0001086631],"domain_scores_gemma":[0.9988054,0.00006114908,0.0003040897,0.00005295493,0.0007534864,0.00002291946],"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":[0.00002640964,0.000004350496,0.001177438,0.00009430622,0.0004710099,0.0000396139,0.00008767146,0.16019,0.8310469,0.002665825,0.00001103194,0.004185431],"study_design_scores_gemma":[0.0008365693,0.00006959981,0.0003257761,0.0003973439,0.00003558809,0.0002446105,0.0001188815,0.236284,0.7598683,0.00008225203,0.001617475,0.0001196181],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9406114,0.001109367,0.05715288,0.00007485652,0.0008723926,0.00003706773,0.00001280933,0.0000455258,0.00008365917],"genre_scores_gemma":[0.9909994,0.000870965,0.008043841,0.000004031991,0.00004818804,6.206797e-7,0.00001336518,0.00001575204,0.000003823701],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.07609406,"threshold_uncertainty_score":0.5008624,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01195159331060322,"score_gpt":0.2414363987589101,"score_spread":0.2294848054483069,"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."}}