{"id":"W4383312054","doi":"10.1016/j.jobe.2023.107279","title":"Explainable ensemble learning data-driven modeling of mechanical properties of fiber-reinforced rubberized recycled aggregate concrete","year":2023,"lang":"en","type":"article","venue":"Journal of Building Engineering","topic":"Innovative concrete reinforcement materials","field":"Engineering","cited_by":90,"is_retracted":false,"has_abstract":false,"ca_institutions":"McMaster University; University of Calgary; Polytechnique Montréal","funders":"","keywords":"Aggregate (composite); Flexural strength; Ultimate tensile strength; Compressive strength; Materials science; Fiber; Properties of concrete; Composite material; Computer science; Environmental science; Structural engineering; Engineering","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.001182698,0.0002619246,0.0008100008,0.0005830661,0.00004411321,0.00004015313,0.0005298707,0.0001216579,0.00002837054],"category_scores_gemma":[0.0006027019,0.0002495765,0.0001283798,0.0005905697,0.0000222974,0.0006371315,0.000233163,0.0003309419,0.000003306879],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009291204,"about_ca_system_score_gemma":0.00005424678,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001300307,"about_ca_topic_score_gemma":8.653711e-8,"domain_scores_codex":[0.9975902,0.0000374813,0.001361168,0.0001527463,0.0004433749,0.0004150125],"domain_scores_gemma":[0.9986646,0.000114167,0.00046768,0.000329236,0.0003438269,0.00008047987],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0000341744,3.599964e-7,7.691659e-7,0.0003129095,0.0001216782,0.000008370789,0.00009785946,0.4993389,0.4997213,0.0002193806,0.0000301167,0.0001141789],"study_design_scores_gemma":[0.0004618412,0.00005629268,5.181937e-7,0.0007955772,0.00003294955,0.00001624945,0.00006238952,0.5696692,0.4284912,0.00000645544,0.0002701251,0.0001370887],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8712041,0.0002248159,0.1276378,0.00001274189,0.0004710518,0.0001281668,0.000007244218,0.0002067542,0.0001073302],"genre_scores_gemma":[0.9886604,0.0002383067,0.01077193,0.000003001041,0.0001388128,0.000005436882,0.0000117572,0.00008633063,0.00008400131],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1174563,"threshold_uncertainty_score":0.9999956,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03906432074191687,"score_gpt":0.2435754083603779,"score_spread":0.204511087618461,"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."}}