{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003868522,0.0007009429,0.0004621617,0.0009475428,0.0001400684,0.000443911,0.0004144356,0.0006019225,0.0006523607],"category_scores_gemma":[0.000998441,0.0002304711,0.0006118868,0.0005695253,0.0001449902,0.0004456175,0.0002162451,0.0003119373,0.0002434252],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003749525,"about_ca_system_score_gemma":0.0006212614,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005999301,"about_ca_topic_score_gemma":0.004340729,"domain_scores_codex":[0.9997469,0.00003750981,0.00002427106,0.00005405267,0.0001082589,0.00002902578],"domain_scores_gemma":[0.9995933,0.0001675669,0.00008877806,0.00002297094,0.0001109974,0.00001640022],"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":[0.00007164627,0.0001047645,0.00781518,0.0000905964,0.00005770691,0.0000803561,0.00002552644,0.8937517,0.01292018,0.0004265461,0.0002876189,0.08436811],"study_design_scores_gemma":[0.000001305284,0.000020606,0.001315162,0.000002886653,0.000003584011,0.000007540313,0.000004258547,0.9965996,0.001881156,0.00009491017,0.00006544834,0.000003505191],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.547251,0.0005501795,0.4459212,0.0001583099,0.00003822213,0.0000687229,0.0002879484,0.00153917,0.004185363],"genre_scores_gemma":[0.9623191,0.0001551601,0.03619479,0.0000166264,0.000009042266,0.00004210159,0.000292895,0.00001660338,0.0009536466],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.005999301,"threshold_uncertainty_score":0.01192874,"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."}}