{"id":"W7141796864","doi":"10.71465/ajccee.173","title":"Application of Machine Learning in Construction Project Management","year":2020,"lang":"","type":"article","venue":"American Journal Of Civil Construction And Environmental Engineering","topic":"Construction Project Management and Performance","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Transformative learning; Project management; Construction management; Construction industry; Workforce; Automation; Key (lock); Analytics; Pre-construction services","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":[0.004116152,0.000550777,0.0004352829,0.001915595,0.0006470735,0.002086608,0.0007211067,0.001067018,0.001552575],"category_scores_gemma":[0.01302274,0.0002271404,0.000330547,0.002585793,0.0008938722,0.00132299,0.001246601,0.001211803,0.0003170454],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002003601,"about_ca_system_score_gemma":0.002593967,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004880207,"about_ca_topic_score_gemma":0.004368054,"domain_scores_codex":[0.9965466,0.002241846,0.0001569243,0.0002315568,0.0006706329,0.0001525297],"domain_scores_gemma":[0.9889008,0.008759987,0.0006205571,0.0004434943,0.001076836,0.0001983329],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.0001367288,0.0004789949,0.02675668,0.000354642,0.0001327524,0.0002468593,0.0004185533,0.4177134,0.001207252,0.04020043,0.004102931,0.5082507],"study_design_scores_gemma":[0.0000195944,0.0001182316,0.005427422,0.000178421,0.00002264666,0.00008284507,0.0003176764,0.9170098,0.002059519,0.06295004,0.01177275,0.00004098169],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1713646,0.01235614,0.73108,0.02006281,0.0005750811,0.0004136931,0.0003238207,0.0007566363,0.06306715],"genre_scores_gemma":[0.9010937,0.00336196,0.09293733,0.000405304,0.0002149169,0.00009496011,0.0001103496,0.00002549823,0.001755956],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004880207,"threshold_uncertainty_score":0.02176851,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01038982322160374,"score_gpt":0.2286101806051678,"score_spread":0.2182203573835641,"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."}}