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Record W6892119611 · doi:10.5061/dryad.5mkkwh73g

Functional genetic diversity of domestic and wild American mink (Neovison vison )

2020· dataset· en· W6892119611 on OpenAlexaffabout

Bibliographic record

VenueZenodo (CERN European Organization for Nuclear Research) · 2020
Typedataset
Languageen
Field
Topic
Canadian institutionsLaurentian UniversityTrent University
Fundersnot available
KeywordsMinkGenetic diversityAmerican minkAlleleGenetic variationLocus (genetics)MicrosatelliteOutbreeding depression

Abstract

fetched live from OpenAlex

The release of domestic organisms to the wild threatens biodiversity because the introduction of domestic genes through interbreeding can negatively impact wild conspecifics via outbreeding depression. In North America, farmed American mink (Neovison vison) frequently escape captivity, yet the impact of these events on functional genetic diversity of wild mink populations is unclear. We characterized domestic and wild mink in Ontario at 17 microsatellites located in functional genes and in a promoter region that is non-coding but thought to be associated with traits affected by domestication. We found low functional genetic diversity in both mink types, as only 4 of 17 genes were variable, and the number of alleles per locus were generally lower in captive mink than in wild mink. To determine if allele frequencies of wild populations were affected by domestic release events, we performed redundancy analysis and spatial analysis of principal components on four polymorphic loci (AR, ATN1, IGF-1, and TOB1). We found evidence to suggest domestic release events are affecting the functional genetic diversity of wild mink, as sPCA showed clear distinctions between wild individuals near mink farms and those located in areas without mink farms. This is further substantiated through RDA, where spatial location was associated with genetic variation of AR, ATN1, and IGF1.

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)0.001
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: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Dataset · Consensus signal: Dataset
Teacher disagreement score0.073
Threshold uncertainty score0.146

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.003
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0050.003

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 head0.034
GPT teacher head0.241
Teacher spread0.208 · 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 designNot applicable
Domainnot available
GenreDataset

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

Citations1
Published2020
Admission routes2
Has abstractyes

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