{"id":"W4360604128","doi":"10.1016/j.mex.2023.102130","title":"Application of multi-agent decision-making methods in hydrological ecosystem services management","year":2023,"lang":"en","type":"article","venue":"MethodsX","topic":"Water resources management and optimization","field":"Engineering","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Analytic hierarchy process; Computer science; Profit (economics); Ecosystem services; Water resources; Decision maker; Process (computing); Operations research; Environmental resource management; Management science; Ecosystem; Environmental science; Ecology; Economics; Engineering","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":[0.001414577,0.000119161,0.000203971,0.0003690534,0.00002508044,0.00001917066,0.0002267123,0.00006453817,0.00001362662],"category_scores_gemma":[0.00001366787,0.0001084149,0.000049918,0.0007389238,0.000008206945,0.00006741765,0.0001363264,0.00006592215,0.00004051853],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004195004,"about_ca_system_score_gemma":5.977732e-7,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000005538587,"about_ca_topic_score_gemma":0.00001429687,"domain_scores_codex":[0.9989332,0.0001810577,0.0003530605,0.0002072514,0.0001345069,0.0001909465],"domain_scores_gemma":[0.9994758,0.0001854179,0.00005487302,0.0002474333,0.00001268253,0.00002379477],"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":[0.000006533581,0.00001387154,0.001188693,0.000316615,0.00002452209,0.000005056371,0.0003384686,0.7235364,0.0004228312,0.0001435429,0.00001186617,0.2739916],"study_design_scores_gemma":[0.0002166882,0.000007741147,0.01051319,0.00007100203,0.00001761279,3.315221e-7,0.0001333948,0.9822739,0.0006171117,0.001058166,0.004984644,0.0001062397],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.06269857,0.00009247693,0.9351372,0.00001035373,0.0001401568,0.000372161,0.000001834388,0.0002985114,0.001248755],"genre_scores_gemma":[0.3449346,0.00005494108,0.6548498,0.00001099178,0.00001403887,0.00007664507,0.000008824964,0.00001775818,0.00003243204],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.2822361,"threshold_uncertainty_score":0.4421029,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0261137511417374,"score_gpt":0.3377573133622746,"score_spread":0.3116435622205372,"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."}}