{"id":"W3100015134","doi":"10.1101/2020.11.10.376350","title":"Mixed-stock analysis in the age of genomics: Rapture genotyping enables evaluation of stock-specific exploitation in a freshwater fish population with weak genetic structure","year":2020,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Fish Ecology and Management Studies","field":"Environmental Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Ministry of Natural Resources and Forestry","funders":"Ohio Sea Grant College, Ohio State University; Old Dominion University","keywords":"Fishing; Fishery; Stock (firearms); Biology; Population; Population genomics; Recreational fishing; Geography; Fisheries management; Stock assessment; Ecology; Genomics; Genome; Demography; Genetics; Gene","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":[0.0009936733,0.0002679328,0.0002083689,0.001157915,0.0003196132,0.0004195177,0.0003224712,0.0002175036,0.0009729048],"category_scores_gemma":[0.001107669,0.0001232871,0.0001702001,0.0005486131,0.0002138064,0.0003195068,0.000466293,0.0001959237,0.0002132974],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002409703,"about_ca_system_score_gemma":0.0001614343,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003778813,"about_ca_topic_score_gemma":0.0130689,"domain_scores_codex":[0.999707,0.00007197035,0.00003524989,0.00008371891,0.00006513858,0.00003677824],"domain_scores_gemma":[0.9993176,0.0001682208,0.000214943,0.00008585343,0.0001486523,0.00006471848],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001738581,0.00005596541,0.8391715,0.00002415302,0.0001292344,0.0001158442,0.0002356461,0.0005859406,0.1432361,0.0002269093,0.0001197472,0.015925],"study_design_scores_gemma":[0.000003955568,0.0001040852,0.979086,0.00000681351,0.00005281348,0.0001459475,0.000178419,0.005825451,0.01415423,0.0001111167,0.000322137,0.000008972321],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.995764,0.00004398083,0.003556785,0.000009963256,0.000001545811,0.000007109995,0.0001627914,0.00001421036,0.0004395513],"genre_scores_gemma":[0.9936393,0.00001370041,0.005762309,0.0000236917,0.000001547737,0.0000212923,0.0001645507,0.000007345731,0.0003663548],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.003778813,"threshold_uncertainty_score":0.007513642,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0242662050154989,"score_gpt":0.2145379808640116,"score_spread":0.1902717758485127,"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."}}