{"id":"W2053272969","doi":"10.1061/(asce)0733-9364(2003)129:4(405)","title":"Predicting Cost Deviation in Reconstruction Projects: Artificial Neural Networks versus Regression","year":2003,"lang":"en","type":"article","venue":"Journal of Construction Engineering and Management","topic":"Construction Project Management and Performance","field":"Decision Sciences","cited_by":140,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo; University of Toronto","funders":"","keywords":"Rework; Artificial neural network; Absolute deviation; Computer science; Standard deviation; Regression analysis; Reliability engineering; Engineering; Data mining; Statistics; Artificial intelligence; Machine learning; Mathematics","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":[0.002533708,0.0007592425,0.0006496357,0.001366226,0.0002205281,0.0008935048,0.000607774,0.0009900909,0.0005056315],"category_scores_gemma":[0.01144273,0.0003645807,0.0004173474,0.001526238,0.0003002408,0.001128987,0.0004162202,0.0009104754,0.0001524539],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007539539,"about_ca_system_score_gemma":0.0005854722,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01388133,"about_ca_topic_score_gemma":0.008063902,"domain_scores_codex":[0.9991147,0.0004206894,0.0000727116,0.0001155563,0.0001930477,0.00008328654],"domain_scores_gemma":[0.9943598,0.004541559,0.0004472334,0.0001123287,0.0004645216,0.00007458405],"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":[0.0001124289,0.0001068136,0.02534007,0.00003556598,0.0000559222,0.0000427269,0.00003309406,0.9430005,0.0002937985,0.000462457,0.0002476397,0.03026902],"study_design_scores_gemma":[0.000002892673,0.00001485854,0.001623207,0.000005014462,0.000003801227,0.000004984938,0.00001518933,0.9978945,0.0001225766,0.0002727385,0.00003647398,0.000003827987],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8396911,0.0006918769,0.1570241,0.0005865787,0.00004149183,0.0000721777,0.0002614274,0.0002587284,0.001372485],"genre_scores_gemma":[0.9814953,0.0003222055,0.01740722,0.00002884897,0.00002029426,0.00004256844,0.00021941,0.0000119321,0.000452247],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01388133,"threshold_uncertainty_score":0.02760106,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05207126550196439,"score_gpt":0.3023404797402309,"score_spread":0.2502692142382665,"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."}}