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Record W2560658716 · doi:10.1139/gen-2016-0163

Microsatellite markers reveal low frequency of natural hybridization between the white-footed mouse (<i>Peromyscus leucopus</i>) and deer mouse (<i>Peromyscus maniculatus</i>) in southern Quebec, Canada

2016· article· en· W2560658716 on OpenAlexaffvenueabout
Sarah S. T. Leo, Virginie Millien

Bibliographic record

VenueGenome · 2016
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGenetic diversity and population structure
Canadian institutionsMcGill University
Fundersnot available
KeywordsPeromyscusBiologyDeer mouseRodentZoologyWhite (mutation)House mouseEcologyGeneticsGene

Abstract

fetched live from OpenAlex

In some parts of southern Quebec, two closely related rodent species - the white-footed mouse (Peromyscus leucopus) and the deer mouse (Peromyscus maniculatus) - have recently come in contact because of climate-driven changes in the distribution of the former. Both species share similar morphology, ecology, and life history traits, which suggests that natural hybridization may be possible. Hybridization among these two species can have important implications on the ecological roles these rodents play in disease transmission, yet few researchers have attempted to examine this phenomenon and results from previous hybridization experiments have remained inconclusive and conflicting. In this study, we attempt to investigate the occurrence of hybridization among white-footed mice and deer mice in southern Quebec by genotyping wild caught specimens with selectively neutral, polymorphic microsatellite markers. Our analyses suggest that hybridization may be occurring at extremely low frequency between both species in our study area. The presence of such hybridization events, even at low frequencies, may have implications on disease transmission risk in the region and further detailed studies are necessary.

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 score0.326
Threshold uncertainty score0.655

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
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 head0.004
GPT teacher head0.176
Teacher spread0.172 · 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

Citations19
Published2016
Admission routes3
Has abstractyes

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