{"id":"W2045782973","doi":"10.1139/f07-141","title":"A statistical modeling method for estimating mortality and abundance of spawning salmon from a time series of counts","year":2008,"lang":"en","type":"article","venue":"Canadian Journal of Fisheries and Aquatic Sciences","topic":"Fish Ecology and Management Studies","field":"Environmental Science","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"California Department of Fish and Game","keywords":"Abundance (ecology); Weir; Statistics; Oncorhynchus; Mortality rate; Maximum likelihood; Mathematics; Series (stratigraphy); Biology; Ecology; Fish <Actinopterygii>; Fishery; Demography; Geography","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":[0.007434989,0.001412946,0.001152281,0.00359406,0.0007956984,0.001222295,0.002322449,0.001296891,0.003582564],"category_scores_gemma":[0.01993201,0.0009843324,0.00204817,0.003428866,0.0008258857,0.001691019,0.001157547,0.002244097,0.00175886],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009794888,"about_ca_system_score_gemma":0.002265002,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006472416,"about_ca_topic_score_gemma":0.005577571,"domain_scores_codex":[0.9960358,0.001660036,0.0003111853,0.0006761502,0.001223998,0.00009282107],"domain_scores_gemma":[0.9885642,0.007465255,0.001397759,0.001215931,0.001231031,0.0001258267],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00007988344,0.0001675025,0.01144819,0.0004092472,0.0007332118,0.0002740493,0.0003450094,0.4006024,0.006283103,0.1360422,0.01175069,0.4318646],"study_design_scores_gemma":[0.0000284761,0.0001005637,0.00291796,0.00007309226,0.0001207078,0.0003714407,0.0000308893,0.9225044,0.002057034,0.05498686,0.0166943,0.0001143449],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0003358335,0.00002713127,0.9991826,0.00003106382,0.00001217804,0.00001977827,0.00008360226,0.0002018369,0.0001059199],"genre_scores_gemma":[0.02707229,0.0002968595,0.9687009,0.0001029391,0.0001513393,0.0009525404,0.0008753632,0.0002091955,0.001638611],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.007434989,"threshold_uncertainty_score":0.03932047,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04138192735146268,"score_gpt":0.2705720765027556,"score_spread":0.2291901491512929,"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."}}