{"id":"W2168920618","doi":"10.1109/icsmc.2006.384558","title":"Computational Intelligence Techniques for Building Transparent Construction Performance Models","year":2006,"lang":"en","type":"article","venue":"","topic":"BIM and Construction Integration","field":"Engineering","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Computer science; Artificial neural network; Cluster analysis; Transparency (behavior); Fuzzy logic; Data mining; Artificial intelligence; Data modeling; Machine learning; Computational intelligence; Genetic algorithm; Model building; Database","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":[],"consensus_categories":[],"category_scores_codex":[0.00003713029,0.00007487462,0.00006312608,0.00006347308,0.0000587348,0.00002341762,0.00003935313,0.00004506102,0.00002704922],"category_scores_gemma":[6.195094e-7,0.00007324421,0.00003197549,0.00007188144,0.00003245053,0.0002201012,0.000001683187,0.00004683478,0.000001886867],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003972112,"about_ca_system_score_gemma":0.000008103957,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000009931832,"about_ca_topic_score_gemma":0.000005944108,"domain_scores_codex":[0.9995768,0.000002642063,0.0001758017,0.00008571285,0.0000685005,0.00009054344],"domain_scores_gemma":[0.9998508,0.00001620413,0.00001449178,0.00004230893,0.00006223812,0.00001392106],"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":[0.000002803596,0.000002992666,0.00007243649,0.00002052184,0.000003446966,2.04267e-8,0.000009554194,0.6563109,0.0004276171,0.2062817,0.0001211501,0.1367469],"study_design_scores_gemma":[0.00003711449,0.00001284003,0.00006255555,0.00001427769,0.000004322984,0.00001255581,0.00002539563,0.8761062,0.06142598,0.0615199,0.0006917715,0.00008709801],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02493832,0.00004040056,0.9669626,0.00002409971,0.0002005991,0.0001423126,0.00000670791,0.0004638226,0.007221174],"genre_scores_gemma":[0.7826723,0.00001376714,0.2171379,0.00000767524,0.00006444041,0.00005090299,0.00001636956,0.000007555783,0.00002910203],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.7577339,"threshold_uncertainty_score":0.2986812,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01874412572166419,"score_gpt":0.2326236221550038,"score_spread":0.2138794964333396,"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."}}