{"id":"W4385520378","doi":"10.1139/cjce-2022-0310","title":"Analysing delay factors in construction projects using Z-number approach: insights from Pakistan","year":2023,"lang":"en","type":"article","venue":"Canadian Journal of Civil Engineering","topic":"Construction Project Management and Performance","field":"Decision Sciences","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Context (archaeology); Procurement; Cost overrun; Prioritization; Project management; Reliability (semiconductor); Government (linguistics); Integrated project delivery; Construction industry; Risk analysis (engineering); Computer science; Business; Operations management; Engineering; Process management; Construction engineering; Systems engineering; Marketing","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007186299,0.0001461328,0.0003012294,0.00241466,0.0001197234,0.000289638,0.0003242535,0.00007202962,0.0002360879],"category_scores_gemma":[0.0003511651,0.0001259905,0.0001125044,0.003041446,0.0000510281,0.0007506852,0.0000232714,0.0002751137,0.00001393525],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002256477,"about_ca_system_score_gemma":0.0004429893,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003178883,"about_ca_topic_score_gemma":0.07440764,"domain_scores_codex":[0.9982125,0.00005189322,0.0007334121,0.000204585,0.0005019068,0.0002957426],"domain_scores_gemma":[0.998929,0.0001743373,0.0002757443,0.0001938729,0.0001667693,0.0002603415],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00001039197,0.000005558658,0.6059332,0.00001717567,0.00009477674,0.0002007024,0.004608098,0.3820144,0.0006417101,0.0009814561,0.0004386765,0.005053917],"study_design_scores_gemma":[0.0009802441,0.00003233519,0.1848536,0.0002785005,0.00007986918,0.0002487177,0.01367741,0.7786425,0.0003502597,0.003072081,0.0170993,0.0006851039],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9825754,0.0000762257,0.01393808,0.00002161562,0.001103037,0.00007409179,0.00000644392,0.00001847437,0.002186612],"genre_scores_gemma":[0.9981984,0.000008093981,0.001579618,0.000009019029,0.000141941,7.664977e-7,0.00000414551,0.00001358263,0.00004447224],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4210795,"threshold_uncertainty_score":0.942482,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07367866405946424,"score_gpt":0.3035676572953858,"score_spread":0.2298889932359216,"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."}}