{"id":"W4409618273","doi":"10.1080/01446193.2025.2479764","title":"Machine learning and optimization strategies for infrastructure projects risk management","year":2025,"lang":"en","type":"article","venue":"Construction Management and Economics","topic":"Construction Project Management and Performance","field":"Decision Sciences","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University","funders":"","keywords":"Risk management; Computer science; Business; Risk analysis (engineering); Engineering management; Engineering; Knowledge management; Finance","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":[0.0008122463,0.0002103394,0.0002471909,0.0008334891,0.0006301375,0.0008913668,0.0001911928,0.00007204198,0.0001103728],"category_scores_gemma":[0.00003978945,0.0001975314,0.0000592004,0.0004173083,0.0001980876,0.0007501286,0.0003019548,0.0001284216,0.00000471045],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004186457,"about_ca_system_score_gemma":0.00002064764,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000007457773,"about_ca_topic_score_gemma":0.0000185174,"domain_scores_codex":[0.9984587,0.00005624254,0.0005299841,0.0006093172,0.0001217444,0.0002239708],"domain_scores_gemma":[0.9992034,0.0001222277,0.0003142992,0.0002331581,0.00007901778,0.00004782457],"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":[0.0000821747,0.000005727219,0.0615184,0.0001075149,0.0001462681,4.193158e-7,0.00009255691,0.02909197,2.520227e-7,0.3322569,0.0006177743,0.57608],"study_design_scores_gemma":[0.001959779,0.00005637882,0.006662612,0.00003386603,0.0002019703,0.000009623453,0.01189038,0.4496465,0.000005996366,0.1818928,0.3472969,0.0003431764],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1435437,0.0008618612,0.6883285,0.0005982918,0.002270344,0.002415866,0.00004034029,0.0002244077,0.1617167],"genre_scores_gemma":[0.5483146,0.03549591,0.38717,0.0004319746,0.0002075092,0.0003445274,0.0000975855,0.00004648215,0.02789148],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.5757368,"threshold_uncertainty_score":0.8595471,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0211767701845107,"score_gpt":0.2836591872622158,"score_spread":0.2624824170777051,"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."}}