{"id":"W2412833024","doi":"10.1061/(asce)co.1943-7862.0001193","title":"Flexible Activity Relations to Support Optimum Schedule Acceleration","year":2016,"lang":"en","type":"article","venue":"Journal of Construction Engineering and Management","topic":"Resource-Constrained Project Scheduling","field":"Decision Sciences","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Schedule; Flexibility (engineering); Computer science; Representation (politics); Relation (database); Resource (disambiguation); Operations research; Range (aeronautics); Process (computing); Logical framework; Industrial engineering; Mathematical optimization; Engineering; Mathematics; Data mining","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":[0.001596035,0.0006833399,0.0005306185,0.000677723,0.0005043518,0.001851439,0.001557256,0.0005911858,0.005724438],"category_scores_gemma":[0.004437631,0.0004869964,0.0008206454,0.001139145,0.001021574,0.002715321,0.00139618,0.001589643,0.0006549101],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001039496,"about_ca_system_score_gemma":0.001720335,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002833178,"about_ca_topic_score_gemma":0.004404086,"domain_scores_codex":[0.99841,0.0005819045,0.0001083073,0.0002346824,0.0004574688,0.0002075818],"domain_scores_gemma":[0.998505,0.0007116242,0.0002894769,0.0002242714,0.0001631804,0.0001064201],"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":[0.0001416746,0.00008998533,0.001120732,0.0001190655,0.00001784632,0.0001342052,0.0002842502,0.7050419,0.006321081,0.2481295,0.001001904,0.03759775],"study_design_scores_gemma":[0.00003620239,0.000173863,0.0005090471,0.00002935345,0.00002666748,0.00008607563,0.0001609223,0.9026116,0.004166697,0.08304807,0.009123906,0.00002764187],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03371674,0.00007879698,0.9588016,0.000116305,0.00002082805,0.000103293,0.0001002596,0.0002911442,0.006771022],"genre_scores_gemma":[0.6234348,0.0002032043,0.371262,0.0000421228,0.00002327096,0.0002550828,0.0002191937,0.0001354556,0.004424876],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005724438,"threshold_uncertainty_score":0.01915014,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05089605280751104,"score_gpt":0.325454514715168,"score_spread":0.274558461907657,"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."}}