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Landscape location affects genetic variation of Canada lynx (<i>Lynx canadensis</i>)

2003· article· en· W2157734540 on OpenAlexaboutno aff
Michael K. Schwartz, L. Scott Mills, Yvette K. Ortega, Leonard F. Ruggiero, Fred W. Allendorf

Bibliographic record

VenueMolecular Ecology · 2003
Typearticle
Languageen
FieldEnvironmental Science
TopicWildlife Ecology and Conservation
Canadian institutionsnot available
Fundersnot available
KeywordsBiologyPopulationGenetic variationRange (aeronautics)MicrosatelliteEvolutionary biologyPopulation geneticsGenetic variabilityEcologyEffective population sizeLoss of heterozygosityAlleleGeneticsDemography

Abstract

fetched live from OpenAlex

The effect of a population's location on the landscape on genetic variation has been of interest to population genetics for more than half a century. However, most studies do not consider broadscale biogeography when interpreting genetic data. In this study, we propose an operational definition of a peripheral population, and then explore whether peripheral populations of Canada lynx (Lynx canadensis) have less genetic variation than core populations at nine microsatellite loci. We show that peripheral populations of lynx have fewer mean numbers of alleles per population and lower expected heterozygosity. This is surprising, given the lynx's capacity to move long distances, but can be explained by the fact that peripheral populations often have smaller population sizes, limited opportunities for genetic exchange and may be disproportionately affected by ebbs and flows of species' geographical range.

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)0.001
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 score0.688
Threshold uncertainty score0.620

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
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 head0.003
GPT teacher head0.163
Teacher spread0.160 · 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

Citations183
Published2003
Admission routes1
Has abstractyes

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