{"id":"W2086442205","doi":"10.1139/l05-003","title":"A neural network model for predicting maximum shear capacity of concrete beams without transverse reinforcement","year":2005,"lang":"en","type":"article","venue":"Canadian Journal of Civil Engineering","topic":"Structural Behavior of Reinforced Concrete","field":"Engineering","cited_by":26,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Cairo University","keywords":"Artificial neural network; Reinforcement; Shear (geology); Transverse plane; Reinforced concrete; Computer science; Structural engineering; Artificial intelligence; Engineering; Geology","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00023682,0.0002698247,0.0004486924,0.0002455885,0.00006367178,0.00003472713,0.0002880702,0.0001279084,0.00005243118],"category_scores_gemma":[0.00005505673,0.0002917599,0.0002427157,0.0001558923,0.00004354372,0.000391714,0.000006652985,0.0003853581,6.513e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003218989,"about_ca_system_score_gemma":0.0001917053,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001854123,"about_ca_topic_score_gemma":0.00136241,"domain_scores_codex":[0.9983032,0.000005984578,0.0007140513,0.0001098321,0.0002202644,0.0006466155],"domain_scores_gemma":[0.998858,0.00004786879,0.0001435407,0.0001848394,0.0001537229,0.0006120041],"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":[0.00001314103,5.602569e-8,0.0001621902,0.0000923613,0.00007766347,0.000006196447,0.0006120331,0.9938595,0.004281763,0.0001072406,0.0002006035,0.0005872559],"study_design_scores_gemma":[0.0007862492,0.00008316959,0.0001029507,0.0001934567,0.00009893175,0.0001055566,0.000032987,0.9934107,0.00373974,0.000016924,0.001163762,0.0002655447],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.64764,0.0005359803,0.350005,0.00004957976,0.0008588931,0.0003460556,0.00005189524,0.0000823347,0.0004302704],"genre_scores_gemma":[0.9823893,0.00001249896,0.01704318,0.00002440806,0.0004239114,0.000008139742,0.000004263906,0.00006833616,0.00002597025],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3347493,"threshold_uncertainty_score":0.9999534,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01409987723746778,"score_gpt":0.1956579247619716,"score_spread":0.1815580475245038,"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."}}