{"id":"W4386523462","doi":"10.1016/j.jclepro.2023.138673","title":"Machine learning models for predicting compressive strength of fiber-reinforced concrete containing waste rubber and recycled aggregate","year":2023,"lang":"en","type":"article","venue":"Journal of Cleaner Production","topic":"Innovative concrete reinforcement materials","field":"Engineering","cited_by":115,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of British Columbia, Okanagan Campus; University of British Columbia","funders":"","keywords":"Compressive strength; Aggregate (composite); Natural rubber; Random forest; Decision tree; Linear regression; Mean squared error; Fiber; Artificial neural network; Computer science; Machine learning; Mathematics; Materials science; Composite material; Statistics","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":[0.0009236726,0.0001715267,0.000425178,0.0002935793,0.00008946683,0.00003712391,0.00008762633,0.00007149785,0.00001868683],"category_scores_gemma":[0.0003392708,0.0001578609,0.00007204525,0.0002021463,0.00004839671,0.0005789531,0.00003927103,0.0002168089,9.945254e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004758513,"about_ca_system_score_gemma":0.00002046127,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000006484191,"about_ca_topic_score_gemma":3.015015e-7,"domain_scores_codex":[0.9985156,0.00005629633,0.0008262407,0.000130803,0.0002392056,0.0002318205],"domain_scores_gemma":[0.9985154,0.0001380239,0.0007239781,0.0001050865,0.000467939,0.00004958332],"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":[0.000345455,4.990501e-7,0.00006007171,0.0002860948,0.0002085959,0.000003467411,0.001160168,0.7635359,0.228953,0.0002343171,0.0002935757,0.00491887],"study_design_scores_gemma":[0.001213213,0.000329022,0.00001632518,0.0004012189,0.00006727366,0.00004197083,0.0007492636,0.6683012,0.3268877,0.0001170048,0.001721012,0.0001547782],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9944575,0.0001879846,0.003617799,0.00007331953,0.0007185066,0.0002645965,0.00001332741,0.0001055036,0.000561456],"genre_scores_gemma":[0.9976797,0.0002700854,0.0007679711,0.000007628343,0.0004882325,0.000008583865,0.00002936219,0.00004657758,0.0007018819],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.09793471,"threshold_uncertainty_score":0.6437381,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02399880235410645,"score_gpt":0.2422074772205857,"score_spread":0.2182086748664793,"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."}}