{"id":"W4405661869","doi":"10.1177/00219983241310556","title":"Application of machine learning for predicting adhesive damage used for joining structural steel with GFRP under hygrothermal effect","year":2024,"lang":"en","type":"article","venue":"Journal of Composite Materials","topic":"Structural Behavior of Reinforced Concrete","field":"Engineering","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Sherbrooke","funders":"","keywords":"Adhesive; Materials science; Composite material; Finite element method; Composite number; Fibre-reinforced plastic; Structural 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006704936,0.0009281213,0.000460012,0.0008014284,0.0001468075,0.0003406526,0.000384141,0.0007743568,0.000453592],"category_scores_gemma":[0.001600313,0.0002404413,0.0004757436,0.0003992902,0.000148778,0.0004173309,0.0002186776,0.0004910687,0.0001905926],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003119254,"about_ca_system_score_gemma":0.000372925,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003055984,"about_ca_topic_score_gemma":0.003186852,"domain_scores_codex":[0.9997647,0.00005628585,0.00001989907,0.00005708391,0.00007442959,0.00002746892],"domain_scores_gemma":[0.9993274,0.0003712737,0.0001166682,0.00003451485,0.0001319463,0.00001813901],"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":[0.0001480783,0.0002161485,0.01302717,0.0001030517,0.00006532192,0.0001210997,0.00005487406,0.8530627,0.02158005,0.0002160578,0.0003423801,0.111063],"study_design_scores_gemma":[0.000001240066,0.00004970982,0.001521411,0.000002930468,0.000004746573,0.00001033214,0.000006486243,0.9950442,0.003231475,0.00006360537,0.00005994314,0.000003980642],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7207617,0.000702882,0.2758372,0.0001296422,0.00004555698,0.0000551003,0.000223343,0.001136853,0.001107789],"genre_scores_gemma":[0.9791399,0.0001092152,0.02001988,0.0000128088,0.00000578213,0.00002759605,0.0001325824,0.00001197732,0.0005402504],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.003055984,"threshold_uncertainty_score":0.006076396,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007296417508779646,"score_gpt":0.2466480149077848,"score_spread":0.2393515973990051,"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."}}