{"id":"W4402679774","doi":"10.1016/j.dibe.2024.100547","title":"Explainable ensemble learning graphical user interface for predicting rebar bond strength and failure mode in recycled coarse aggregate concrete","year":2024,"lang":"en","type":"article","venue":"Developments in the Built Environment","topic":"Recycled Aggregate Concrete Performance","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"Polytechnique Montréal; University of Guelph; University of Calgary","funders":"","keywords":"Rebar; Aggregate (composite); Failure mode and effects analysis; Mode (computer interface); Bond strength; Interface (matter); Computer science; Graphical user interface; Materials science; Composite material; Structural engineering; Engineering; Adhesive; Human–computer interaction; Layer (electronics)","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":[0.0005479797,0.0003155541,0.0002762686,0.0002057469,0.0001065273,0.0001154935,0.0002834472,0.0001502282,0.00001585785],"category_scores_gemma":[0.00005596987,0.0002722552,0.00004237809,0.0002185257,0.00007147288,0.0003050671,0.0001415534,0.000559038,0.00001574044],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002764618,"about_ca_system_score_gemma":0.0000213836,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003267062,"about_ca_topic_score_gemma":0.00004399141,"domain_scores_codex":[0.9982023,0.00007284091,0.0004733908,0.0004415963,0.0002602884,0.0005495967],"domain_scores_gemma":[0.9993619,0.0002761051,0.00004560796,0.0002401442,0.000005166299,0.00007109205],"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":[0.0007605033,0.000112551,0.1092934,0.004039324,0.001045551,0.0008900945,0.09810819,0.3709078,0.1141119,0.003287869,0.00602152,0.2914212],"study_design_scores_gemma":[0.002459454,0.0001472281,0.004379432,0.001606392,0.00005246959,0.00009831994,0.003970543,0.7988794,0.0380406,0.0007828777,0.1484357,0.00114757],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9931788,0.001824309,0.003417048,0.0001958182,0.0001810204,0.0005737247,0.00001051534,0.0001374608,0.0004812529],"genre_scores_gemma":[0.9902332,0.001771303,0.007200132,0.0000312051,0.00003354689,0.000343287,0.00002582941,0.00006950994,0.0002920055],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4279715,"threshold_uncertainty_score":0.9999729,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008109864821849479,"score_gpt":0.2232041004415558,"score_spread":0.2150942356197063,"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."}}