{"id":"W4396577292","doi":"10.1016/j.ecolmodel.2024.110738","title":"Development of a fuzzy logic-embedded system dynamics model to simulate complex socio-ecological systems","year":2024,"lang":"en","type":"article","venue":"Ecological Modelling","topic":"Sustainability and Ecological Systems Analysis","field":"Environmental Science","cited_by":9,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"National Research Foundation of Korea; Ministry of Education; CHEO Research Institute","keywords":"Fuzzy logic; Adaptive neuro fuzzy inference system; Computer science; Reliability (semiconductor); Inference; Data mining; Machine learning; Fuzzy control system; Artificial intelligence","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.001779577,0.0004090981,0.0009148726,0.000111279,0.0004418642,0.0001424296,0.00059608,0.0004902663,0.0006262796],"category_scores_gemma":[0.0001181071,0.0002929004,0.0003238112,0.0007294854,0.0002058688,0.000161102,0.0006699447,0.000353935,0.0007506092],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002781468,"about_ca_system_score_gemma":0.00006364041,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00009355392,"about_ca_topic_score_gemma":0.00008918815,"domain_scores_codex":[0.9958927,0.0002267194,0.001308271,0.001077137,0.0006047561,0.0008904233],"domain_scores_gemma":[0.9985717,0.0004486588,0.0001697207,0.0003712749,0.00005949565,0.0003791705],"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":[0.00003282046,0.0002822129,0.0006743653,0.0002345331,0.00006740901,0.00005452193,0.0006984771,0.9796246,0.0002298997,0.01746381,0.0001067201,0.0005306727],"study_design_scores_gemma":[0.0001397989,0.0001552685,0.0009897868,0.00005098879,0.00005311776,0.000007163216,0.002434272,0.9915685,0.000009492514,0.003581257,0.0006109591,0.0003993643],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6367276,0.00004552967,0.3569544,0.0001694033,0.0001554242,0.0007150421,0.00001392271,0.000291585,0.004927123],"genre_scores_gemma":[0.9638359,0.000004519034,0.03524362,0.0001223968,0.00004869265,0.0002086942,0.00002210798,0.00002315715,0.0004909176],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3271083,"threshold_uncertainty_score":0.9999523,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05714529749154915,"score_gpt":0.2732677994285133,"score_spread":0.2161225019369642,"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."}}