{"id":"W1982469114","doi":"10.1002/sdr.359","title":"Understanding and managing iterative error and change cycles in construction","year":2007,"lang":"en","type":"article","venue":"System Dynamics Review","topic":"Construction Project Management and Performance","field":"Decision Sciences","cited_by":48,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"Seoul National University","keywords":"Schedule; Scope (computer science); Process (computing); Computer science; Project management; Risk analysis (engineering); System dynamics; Contingency; Quality (philosophy); Operations research; Process management; Systems engineering; Engineering; Artificial intelligence","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":[0.002360131,0.0005532886,0.0004915464,0.001324354,0.0006638796,0.002316563,0.001108093,0.001186419,0.002135021],"category_scores_gemma":[0.01170401,0.0004443173,0.0003875147,0.000946135,0.001685895,0.003921479,0.001945777,0.0007288227,0.0001768731],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002570616,"about_ca_system_score_gemma":0.003101174,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01367979,"about_ca_topic_score_gemma":0.008387696,"domain_scores_codex":[0.9981197,0.0009311383,0.00008182901,0.0002004412,0.0005073976,0.0001595593],"domain_scores_gemma":[0.9947987,0.00325927,0.0009921073,0.0002729164,0.0004966437,0.0001804105],"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":[0.00006133601,0.00006972414,0.009037904,0.0001181439,0.00003984471,0.0001594301,0.001133866,0.9044031,0.0008522522,0.03828999,0.0004182513,0.04541611],"study_design_scores_gemma":[0.00001761817,0.00006446087,0.003189583,0.00004877051,0.00002147337,0.0000836531,0.0009496638,0.9434589,0.0007658859,0.04750831,0.003867011,0.00002462784],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3621681,0.0007436051,0.6180419,0.001762978,0.00003092628,0.0002033625,0.000104563,0.0002890952,0.0166555],"genre_scores_gemma":[0.9686453,0.0004338902,0.02960293,0.0000239275,0.000007498967,0.00007954017,0.00005564987,0.00002150235,0.001129687],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01367979,"threshold_uncertainty_score":0.02720034,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2791405088837867,"score_gpt":0.4029536917042426,"score_spread":0.1238131828204559,"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."}}