{"id":"W4388834997","doi":"10.3390/su152216126","title":"Advancing Shear Capacity Estimation in Rectangular RC Beams: A Cutting-Edge Artificial Intelligence Approach for Assessing the Contribution of FRP","year":2023,"lang":"en","type":"article","venue":"Sustainability","topic":"Structural Behavior of Reinforced Concrete","field":"Engineering","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University; University of Toronto","funders":"","keywords":"Fibre-reinforced plastic; Structural engineering; Computer science; Artificial neural network; AdaBoost; Machine learning; Support vector machine; Artificial intelligence; 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":[0.0007881502,0.0009257756,0.0005782879,0.001112696,0.000197214,0.0004620738,0.0007927415,0.0007823235,0.0008078226],"category_scores_gemma":[0.001983926,0.0003195245,0.0004841148,0.0006523115,0.0004432561,0.0007070026,0.0004583175,0.0006255773,0.0003713913],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003138898,"about_ca_system_score_gemma":0.000655866,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003122037,"about_ca_topic_score_gemma":0.003612836,"domain_scores_codex":[0.9997379,0.00005687225,0.00001326388,0.00006755996,0.00009815386,0.0000262527],"domain_scores_gemma":[0.999137,0.0003746407,0.0001738509,0.00008827557,0.0002026932,0.00002354456],"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":[0.00009341937,0.00007482842,0.007551492,0.0001325267,0.00005140739,0.0001452083,0.0000591348,0.8571027,0.01771038,0.001877039,0.0005206041,0.1146813],"study_design_scores_gemma":[0.000001376982,0.00003048742,0.001231497,0.00001418806,0.000008976877,0.00002464458,0.00001300426,0.9933568,0.004404808,0.0006580781,0.0002485914,0.000007600115],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1727693,0.0007981774,0.8214535,0.0002646739,0.00004774021,0.00005497626,0.0001530952,0.0007120679,0.00374652],"genre_scores_gemma":[0.9252512,0.000381325,0.07313289,0.00006558297,0.00002412456,0.00004398005,0.0001425975,0.00003149095,0.0009267002],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003122037,"threshold_uncertainty_score":0.006207705,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0174016339189724,"score_gpt":0.2962359746117602,"score_spread":0.2788343406927877,"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."}}