{"id":"W1974249820","doi":"10.1016/j.fishres.2011.11.006","title":"Accounting for spatial population structure at scales relevant to life history improves stock assessment: The case for Lake Erie walleye Sander vitreus","year":2011,"lang":"en","type":"article","venue":"Fisheries Research","topic":"Fish Ecology and Management Studies","field":"Environmental Science","cited_by":60,"is_retracted":false,"has_abstract":false,"ca_institutions":"Ministry of Natural Resources and Forestry","funders":"Innovative Research Group Project of the National Natural Science Foundation of China; Ministry of Natural Resources; Michigan Department of Natural Resources; New York State Department of Environmental Conservation","keywords":"Stock assessment; Biological dispersal; Stock (firearms); Fishery; Sander; Catch per unit effort; Fishing; Spatial ecology; Population; Temporal scales; Overfishing; Geography; Ecology; Biology; Demography","routes":{"ca_aff":true,"ca_fund":true,"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":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0007217207,0.0001430196,0.0001603222,0.0000514015,0.001268769,0.00003889305,0.0003022967,0.00009745635,0.003709078],"category_scores_gemma":[0.0005320688,0.0001059125,0.00005628287,0.000102955,0.0003443979,0.0002538204,0.0007770692,0.0001829966,0.00001738412],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004884124,"about_ca_system_score_gemma":0.00002105052,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001238268,"about_ca_topic_score_gemma":0.3304258,"domain_scores_codex":[0.9984828,0.0001017351,0.0002101897,0.0003981985,0.0002845381,0.0005225419],"domain_scores_gemma":[0.9991636,0.0003332932,0.00007161734,0.0002985088,0.00005180625,0.000081135],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"observational","study_design_scores_codex":[0.0006000987,0.00007645138,0.2797493,0.0001444308,0.00009001165,0.00002627317,0.00312535,0.00002787724,0.000559542,0.0002940305,0.7067145,0.008592139],"study_design_scores_gemma":[0.0003412742,0.0003022337,0.6917765,0.000004226641,0.00001861435,0.000005083631,0.0007088336,0.0006541387,0.00004436095,0.001125832,0.3048669,0.0001520599],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9844888,0.00003282939,0.001020605,0.003333022,0.0005081283,0.002629437,0.0001539034,0.00004948127,0.007783771],"genre_scores_gemma":[0.9901034,0.00001264372,0.002801598,0.0006229452,0.0001383263,0.0008294012,0.00006224653,0.00002689438,0.005402494],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4120272,"threshold_uncertainty_score":0.9972017,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06736063440937698,"score_gpt":0.3108296357183806,"score_spread":0.2434690013090036,"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."}}