{"id":"W2255928510","doi":"","title":"A Strategy For Collecting And Evaluating Information On Engineering Consultant Offices Using Neural Networks: A Case Study","year":2006,"lang":"en","type":"article","venue":"Proceedings of the Joint CIB W78, W102, ICCCBE, ICCC, and DMUCE International Conference on Computing and Decision Making in Civil and Building Engineering, Montreal, Canada, 14-16 June","topic":"BIM and Construction Integration","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Standardization; Artificial neural network; Computer science; Process (computing); Field (mathematics); The Internet; Software engineering; Interface (matter); Information engineering; Information system; Artificial intelligence; Engineering management; Engineering; World Wide Web","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.008707971,0.0007282659,0.0005835273,0.005288639,0.002387622,0.002354765,0.001696972,0.001971787,0.00239966],"category_scores_gemma":[0.0186893,0.0004955971,0.0005858048,0.003760484,0.0009540438,0.003123993,0.001921888,0.001053423,0.0005874794],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.004056306,"about_ca_system_score_gemma":0.003233849,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01433652,"about_ca_topic_score_gemma":0.0257061,"domain_scores_codex":[0.9835387,0.0103707,0.0007982447,0.0009010824,0.00374518,0.0006460057],"domain_scores_gemma":[0.9811907,0.009585404,0.001314322,0.001609903,0.00549354,0.0008060096],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.001353242,0.005200464,0.09363224,0.0008335789,0.0002364518,0.009225192,0.01792977,0.06594157,0.02043566,0.02987947,0.01535127,0.7399811],"study_design_scores_gemma":[0.0005600119,0.00469255,0.0642001,0.0008222179,0.0004442438,0.005250101,0.062944,0.6628073,0.09163162,0.01787427,0.08811398,0.0006595746],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7321902,0.0005836131,0.2224571,0.003386088,0.00007003203,0.002441278,0.0007376467,0.0008280848,0.03730601],"genre_scores_gemma":[0.6929254,0.0003505406,0.2991378,0.000350098,0.00004217499,0.0005486555,0.0004386863,0.00007584639,0.006130793],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01433652,"threshold_uncertainty_score":0.04605269,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02475945567403902,"score_gpt":0.268776952283782,"score_spread":0.2440174966097429,"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."}}