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Record W4390437070 · doi:10.3390/fishes9010015

Population Genetic Differentiation of Walleye (Sander vitreus) across the Eastern Highlands of the United States

2023· article· en· W4390437070 on OpenAlexfundno aff
Sheila C. Harris, George Palmer, Carol A. Stepien, Eric M. Hallerman

Bibliographic record

VenueFishes · 2023
Typearticle
Languageen
FieldEnvironmental Science
TopicFish Ecology and Management Studies
Canadian institutionsnot available
FundersU.S. Fish and Wildlife ServiceU.S. Department of AgricultureAuburn UniversityVirginia Department of Game and Inland FisheriesNational Institute of Food and AgricultureMichigan Department of Natural ResourcesMinistry of Natural Resources
KeywordsSanderRange (aeronautics)StockingGeographyBayFisheryPopulationEcologyDrainage basinBiologyArchaeologyDemography

Abstract

fetched live from OpenAlex

Walleye is an important sportfish across eastern North America, is commercially fished in the Laurentian Great Lakes region, and has been introduced outside its native range. Thirty-eight Walleye populations within six watersheds across the Eastern Highlands and other portions of the native range were screened at eight microsatellite DNA loci to better understand evolutionary history and to inform fishery management and conservation efforts. Population genetic variation showed divergent assemblages of populations, respectively, living in the Mobile Bay, Mississippi River, Eastern Highlands (Tennessee, New, and Ohio Rivers), and Great Lakes drainages today. All estimates of effective numbers of breeding individuals were under 25, and all populations within all watersheds had ~15–20% inter-individual relatedness, likely attributable to the effects of both natural demographic processes and stocking. The extent of Eastern Highlands Walleye includes both the Ohio and Tennessee River basins.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.045
Threshold uncertainty score0.089

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.012
GPT teacher head0.227
Teacher spread0.215 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations4
Published2023
Admission routes1
Has abstractyes

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