{"id":"W2126524344","doi":"10.1577/t07-068.1","title":"How Systematic Age Underestimation Can Impede Understanding of Fish Population Dynamics: Lessons Learned from a Lake Superior Cisco Stock","year":2008,"lang":"en","type":"article","venue":"Transactions of the American Fisheries Society","topic":"Fish Ecology and Management Studies","field":"Environmental Science","cited_by":82,"is_retracted":false,"has_abstract":true,"ca_institutions":"Ministry of Natural Resources and Forestry","funders":"Ministry of Natural Resources; Minnesota Department of Natural Resources; U.S. Geological Survey; Cisco Systems","keywords":"Otolith; Fishing; Fishery; Coregonus; Demography; Biology; Bay; Mark and recapture; Survivorship curve; Mortality rate; Stock assessment; Stock (firearms); Vital rates; Fish <Actinopterygii>; Fish stock; Population; Geography","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true,"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":[0.009638161,0.0005656379,0.0005103674,0.000507379,0.0008607242,0.001392555,0.001333944,0.0006366716,0.001312325],"category_scores_gemma":[0.02847724,0.0002980712,0.0003440872,0.0006067218,0.001105223,0.002463432,0.001126467,0.0008071389,0.0001713757],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002322455,"about_ca_system_score_gemma":0.002566117,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.3238777,"about_ca_topic_score_gemma":0.4220383,"domain_scores_codex":[0.9988227,0.0004978136,0.00008013696,0.0002559916,0.0002677784,0.00007551316],"domain_scores_gemma":[0.9869632,0.005404742,0.00145506,0.001444349,0.004243997,0.0004886654],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001056397,0.00005021852,0.911429,0.0001045842,0.0001383352,0.0004684612,0.006533701,0.005578639,0.001189911,0.0009408761,0.001926207,0.07153444],"study_design_scores_gemma":[0.00003258645,0.0003287912,0.9206579,0.0005811501,0.0002461469,0.0004081365,0.008741491,0.05338621,0.002187356,0.00538043,0.007924609,0.0001252659],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9883764,0.001274256,0.004434695,0.003152612,0.00004009814,0.00002273651,0.0001677998,0.00003085685,0.002500462],"genre_scores_gemma":[0.9934128,0.001067982,0.004261804,0.0003778706,0.00004404644,0.00001432673,0.0001136052,0.00001748991,0.0006900248],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3238777,"threshold_uncertainty_score":0.6439851,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05413464480927197,"score_gpt":0.245895590499114,"score_spread":0.1917609456898421,"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."}}