{"id":"W2952003046","doi":"10.1061/9780784482421.072","title":"Integrating Fuzzy Agent-Based Modeling and Multi-Criteria Decision-Making for Analyzing Construction Crew Performance","year":2019,"lang":"en","type":"article","venue":"","topic":"BIM and Construction Integration","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Natural Sciences and Engineering Research Council of Canada; Canadian Natural Resources; University of Alberta","funders":"","keywords":"Crew; Decision support system; Multiple-criteria decision analysis; Fuzzy logic; Computer science; Operations research; Fuzzy set; Risk analysis (engineering); Management science; Engineering; Artificial intelligence; Business","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":[0.003245237,0.0009146698,0.0009826148,0.001730305,0.0007077715,0.002343929,0.001374321,0.001293313,0.001234831],"category_scores_gemma":[0.004617997,0.0004395814,0.0009228887,0.001337967,0.0007669309,0.001423072,0.001046186,0.0009869923,0.0001580483],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001833055,"about_ca_system_score_gemma":0.002035703,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006992221,"about_ca_topic_score_gemma":0.008898098,"domain_scores_codex":[0.998242,0.001112041,0.00008258095,0.0001131665,0.0003709343,0.00007925026],"domain_scores_gemma":[0.9979041,0.001476462,0.0002302737,0.00008796398,0.0002250549,0.00007617986],"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":[0.00003475502,0.00007548759,0.001179709,0.00009622359,0.00009084967,0.00008593135,0.0001376112,0.946927,0.0008582317,0.03150358,0.0002408309,0.01876983],"study_design_scores_gemma":[0.000007807367,0.00002477494,0.0001392352,0.00001866487,0.00001199766,0.00001096871,0.00005040145,0.9879256,0.0002454568,0.01093517,0.0006179565,0.00001200024],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02351082,0.0002210581,0.9722892,0.0003091304,0.00003247264,0.000112625,0.00005134413,0.00007138801,0.003401966],"genre_scores_gemma":[0.579053,0.0004065229,0.4190039,0.00007541204,0.00003198962,0.0003415678,0.00007754364,0.00001998092,0.000990211],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006992221,"threshold_uncertainty_score":0.01716262,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01430058495238221,"score_gpt":0.2539166227715008,"score_spread":0.2396160378191186,"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."}}