{"id":"W3149105485","doi":"10.1109/wsc.2007.4419847","title":"Qualitative simulation of construction performance using fuzzy cognitive maps","year":2007,"lang":"en","type":"article","venue":"2007 Winter Simulation Conference","topic":"Cognitive Science and Mapping","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Fuzzy cognitive map; Computer science; Process (computing); Fuzzy logic; Artificial intelligence; Fuzzy set; Industrial engineering; Machine learning; Engineering; Fuzzy number","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":[0.001081386,0.0001729151,0.0002070381,0.0003244579,0.0001576447,0.00008528191,0.0002946231,0.00008169441,0.00004985617],"category_scores_gemma":[0.0002878265,0.000177605,0.00006652543,0.0006266281,0.0002319443,0.001579236,0.0001236515,0.0001352895,0.00003374239],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006529753,"about_ca_system_score_gemma":0.000117203,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001579059,"about_ca_topic_score_gemma":0.000006486841,"domain_scores_codex":[0.9982025,0.0001145943,0.0005212105,0.0004171576,0.000426562,0.0003179889],"domain_scores_gemma":[0.9971495,0.0008544668,0.0003665063,0.0002146702,0.001325918,0.00008889639],"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":[0.0004559636,0.0002774775,0.01586842,0.0001907241,0.0001362715,0.0000154069,0.08930027,0.4459074,0.009473743,0.03939201,0.00002655593,0.3989558],"study_design_scores_gemma":[0.000542237,0.000114225,0.006069797,0.0002387878,0.00001397357,0.000004798532,0.003465592,0.9811053,0.006142844,0.001917589,0.0001323337,0.0002525351],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3026868,0.00001017243,0.6925178,0.00002218289,0.0004901087,0.0001815831,0.000006154228,0.00005299958,0.004032242],"genre_scores_gemma":[0.9867643,0.000002708199,0.01292979,0.0001065378,0.0001144586,0.000001796884,0.000009004525,0.000006557775,0.00006486149],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6840775,"threshold_uncertainty_score":0.724252,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1055461053541458,"score_gpt":0.3900350706930644,"score_spread":0.2844889653389186,"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."}}