{"id":"W4387986746","doi":"10.7712/120123.10474.21197","title":"PREDICTION OF REINFORCED CONCRETE COLUMNS LIMIT STATES USING MACHINE LEARNING ALGORITHM","year":2023,"lang":"en","type":"article","venue":"COMPDYN Proceedings","topic":"Structural Behavior of Reinforced Concrete","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Berger (Canada); York University","funders":"","keywords":"Brittleness; Structural engineering; Ductility (Earth science); Nonlinear system; Computer science; Experimental data; Shear strength (soil); Shear (geology); Strength of materials; Reinforcement; Materials science; Engineering; Geology; Mathematics; Composite material; Physics; Creep","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":[0.0004502531,0.0006093328,0.0006383492,0.0007964677,0.0002687671,0.0004163969,0.0004720683,0.0007965071,0.0008577319],"category_scores_gemma":[0.001940332,0.000314301,0.0004212764,0.0003948761,0.0002550319,0.0005508176,0.0003029789,0.0005402059,0.0002591859],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004784147,"about_ca_system_score_gemma":0.0005442128,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007378625,"about_ca_topic_score_gemma":0.006291139,"domain_scores_codex":[0.9997943,0.0000375249,0.00001666382,0.00006768882,0.00005106585,0.0000327431],"domain_scores_gemma":[0.9986524,0.0007822843,0.0001295432,0.00006864964,0.0003218187,0.00004541032],"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":[0.00009682217,0.0001003913,0.004453436,0.00001838123,0.00001993113,0.000047521,0.00001373976,0.9540059,0.003306384,0.0003478567,0.0003507464,0.03723889],"study_design_scores_gemma":[8.114305e-7,0.000004171952,0.0002673935,6.041656e-7,7.90627e-7,0.000001476297,6.614945e-7,0.9993636,0.0002894554,0.00006154087,0.000008395135,0.00000106413],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.568479,0.0002540899,0.4274895,0.000119554,0.00004239126,0.00005112498,0.0002738485,0.001385923,0.001904487],"genre_scores_gemma":[0.9693627,0.0000379936,0.02949165,0.0000121895,0.000007596126,0.00004178869,0.0002433377,0.00001390578,0.0007887591],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.007378625,"threshold_uncertainty_score":0.01467139,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02595448757145467,"score_gpt":0.2268394640280187,"score_spread":0.2008849764565641,"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."}}