{"id":"W4405548159","doi":"10.1016/j.jenvman.2024.123666","title":"Decision analysis of Integrated Pest Management: A case study on invasive sea lamprey in the Great Lakes Basin","year":2024,"lang":"en","type":"article","venue":"Journal of Environmental Management","topic":"Fish Ecology and Management Studies","field":"Environmental Science","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"Fisheries and Oceans Canada","funders":"Michigan Department of Natural Resources; Quantitative Fisheries Center; Michigan State University; Great Lakes Fishery Commission","keywords":"Lamprey; Structural basin; Integrated pest management; Invasive species; PEST analysis; Fishery; Environmental science; Geography; Ecology; Environmental resource management; Biology","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":[0.005064302,0.0007249822,0.0005702551,0.0008309103,0.001632123,0.001683856,0.00106077,0.001550665,0.001583777],"category_scores_gemma":[0.00881787,0.0003250603,0.000841458,0.0009188337,0.001536754,0.001086529,0.00144579,0.001242976,0.00005803634],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00517597,"about_ca_system_score_gemma":0.003211342,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03497896,"about_ca_topic_score_gemma":0.06571416,"domain_scores_codex":[0.9971012,0.002005726,0.00008913763,0.0002130103,0.0002509972,0.0003398514],"domain_scores_gemma":[0.9914521,0.007214453,0.0004525504,0.0002249732,0.0003695282,0.0002863583],"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":[0.0008370397,0.002096415,0.05857425,0.0004571764,0.0003435627,0.00428485,0.003285448,0.8454762,0.00491831,0.02427879,0.001394779,0.05405309],"study_design_scores_gemma":[0.0002699717,0.001022932,0.02157223,0.00007632263,0.0001592482,0.0003175204,0.005355449,0.9461657,0.002818031,0.0190132,0.003131385,0.00009800208],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9849036,0.0001485005,0.01079688,0.0004801334,0.000004921892,0.000230062,0.0000836995,0.00002722196,0.003324888],"genre_scores_gemma":[0.9832449,0.0001161951,0.01583163,0.00004518345,0.00000620854,0.0001162035,0.00005110823,0.000007239099,0.0005813791],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03497896,"threshold_uncertainty_score":0.06955069,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01091616920129883,"score_gpt":0.238202943130491,"score_spread":0.2272867739291921,"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."}}