{"id":"W2287476723","doi":"10.1139/cjfas-2015-0191","title":"Exploring optimal walleye exploitation rates for northern Wisconsin Ceded Territory lakes using a hierarchical Bayesian age-structured model","year":2016,"lang":"en","type":"article","venue":"Canadian Journal of Fisheries and Aquatic Sciences","topic":"Fish Ecology and Management Studies","field":"Environmental Science","cited_by":40,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"College of Engineering, Michigan State University; U.S. Fish and Wildlife Service; Wisconsin Department of Natural Resources; Michigan State University","keywords":"Recreation; Stock assessment; Fishing; Fishery; Recreational fishing; Stock (firearms); Geography; Population; Biomass (ecology); Fisheries management; Ecology; Environmental science; Biology; Demography","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009044782,0.000355379,0.0004511261,0.0005356667,0.0004399698,0.0007882392,0.0007364482,0.0006277224,0.0007441974],"category_scores_gemma":[0.001981016,0.0004863641,0.0006559373,0.0003171651,0.0003989656,0.0006709967,0.0005602503,0.000309747,0.00006212219],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001585206,"about_ca_system_score_gemma":0.00151922,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.120849,"about_ca_topic_score_gemma":0.1388537,"domain_scores_codex":[0.9998264,0.00006516394,0.000008412137,0.00004399712,0.00001162873,0.00004436808],"domain_scores_gemma":[0.9995574,0.0002264871,0.00008955206,0.00001950441,0.00005015268,0.00005677995],"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":[0.00005126745,0.00002473971,0.02313124,0.000008038812,0.00005285093,0.00005342198,0.00006734912,0.9725858,0.0004654787,0.001681555,0.00008199127,0.00179621],"study_design_scores_gemma":[0.00001167725,0.00001687593,0.004398211,0.00000375844,0.0000215428,0.000006724104,0.00002970506,0.9948143,0.00006974377,0.0005632048,0.00005733799,0.00000702701],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9878786,0.00002846344,0.01130798,0.00007053877,0.000001354452,0.00001056965,0.0001253393,0.00001560464,0.0005614551],"genre_scores_gemma":[0.9955475,0.0000349024,0.0037283,0.00001243162,0.000001112196,0.00001978009,0.0001326024,0.000004036707,0.0005192927],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.120849,"threshold_uncertainty_score":0.2402912,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06309760425227012,"score_gpt":0.2479415916690071,"score_spread":0.184843987416737,"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."}}