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Record W2046095612 · doi:10.1577/m05-190.1

Conservation Genetics of Lake Superior Brook Trout: Issues, Questions, and Directions

2008· article· en· W2046095612 on OpenAlexaff
Chris C. Wilson, Wendylee Stott, Loren M. Miller, Silvia D'Amelio, Martin J. Jennings, Anne Cooper

Bibliographic record

VenueNorth American Journal of Fisheries Management · 2008
Typearticle
Languageen
FieldEnvironmental Science
TopicFish Ecology and Management Studies
Canadian institutionsTrent UniversityMinistry of Natural Resources and Forestry
FundersU.S. Geological SurveyU.S. Fish and Wildlife ServiceMinnesota Department of Natural Resources
KeywordsSalvelinusTroutStockingGenetic diversityBiologyGenetic monitoringPopulationEcologyHatcheryHabitatFisheryFish <Actinopterygii>MicrosatelliteDemography

Abstract

fetched live from OpenAlex

Abstract Parallel efforts by several genetic research groups have tackled common themes relating to management concerns about and recent rehabilitation opportunities for coaster brook trout Salvelinus fontinalis in Lake Superior. The questions that have been addressed include the evolutionary and genetic status of coaster brook trout, the degree of relatedness among coaster populations and their relationship to riverine tributary brook trout populations, and the role and effectiveness of stocking in maintaining and restoring coasters to Lake Superior. Congruent genetic results indicate that coasters are an ecotype (life history variant) rather than an evolutionarily significant unit or genetically distinct strain. Regional structure exists among brook trout stocks, coasters being produced from local populations. Introgression of hatchery genes into wild populations appears to vary regionally and may relate to local population size, habitat integrity, and anthropogenic pressures. Tracking the genetic diversity and integrity associated with captive breeding programs is helping to ensure that the fish used for stocking are representative of their source populations and appropriate for rehabilitation efforts. Comparative analysis of shared samples among collaborating laboratories is enabling standardization of genotype scoring and interpretation as well as the development of a common toolkit for assessing genetic structure and diversity. Incorporation of genetic data into rehabilitation projects will facilitate monitoring efforts and subsequent adaptive management. Together, these multifaceted efforts provide comprehensive insights into the biology of coaster brook trout and enhance restoration options.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.190
Threshold uncertainty score0.423

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.009
GPT teacher head0.205
Teacher spread0.197 · 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 teacher head, 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

Citations33
Published2008
Admission routes1
Has abstractyes

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