{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003614511,0.0001120687,0.0003005371,0.000412999,0.0001285597,0.0001397127,0.0001022976,0.00003449857,0.00001632211],"category_scores_gemma":[0.00007115918,0.00008821338,0.00002875401,0.0007172085,0.00009748018,0.0004572116,0.00008946664,0.00008616089,0.000009091128],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001841325,"about_ca_system_score_gemma":0.000007885784,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003354027,"about_ca_topic_score_gemma":0.0007835052,"domain_scores_codex":[0.9985609,0.0001058373,0.0005522697,0.0002986894,0.0003179779,0.0001642963],"domain_scores_gemma":[0.9993253,0.0001792509,0.0002305469,0.0001697888,0.00004805046,0.00004705124],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00001204009,0.000004128221,0.1577798,0.00274992,0.00001421632,0.00001776912,0.0007939399,0.000001841701,0.000001390597,0.2893625,0.00004866975,0.5492138],"study_design_scores_gemma":[0.002988623,0.000211025,0.1837028,0.05488475,0.0002581253,0.002230653,0.120439,0.5574545,0.000005854435,0.05434854,0.02134264,0.002133557],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.3746078,0.1622638,0.3206937,0.007354959,0.004039517,0.006995653,0.00005048213,0.0003294634,0.1236646],"genre_scores_gemma":[0.9880837,0.01091754,0.0007070534,0.0001320684,0.00003327501,0.00001411989,0.000002830959,0.000005952472,0.000103457],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6134759,"threshold_uncertainty_score":0.3597237,"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."}}