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Record W6923650814 · doi:10.14288/1.0397856

Data from: Life-history characteristics and landscape attributes as drivers of genetic variation, gene flow and fine-scale population structure in Northern Dolly Varden (Salvelinus malma malma) in Canada

2020· dataset· en· W6923650814 on OpenAlexaboutno aff

Bibliographic record

VenueOpen Collections · 2020
Typedataset
Languageen
Field
Topic
Canadian institutionsnot available
Fundersnot available
KeywordsSympatric speciationFish migrationPopulationGenetic structureArcticGene flowHabitatContext (archaeology)Salvelinus

Abstract

fetched live from OpenAlex

<b>Abstract</b><br/>The Northern Dolly Varden (Salvelinus malma malma) displays variable life-history types and occupies freshwater habitats with varying levels of connectivity. Here, we assayed microsatellite DNA variation in Northern Dolly Varden from the western Canadian Arctic to resolve landscape and life history variables driving variation in genetic diversity and population structure. Overall, genetic variation was highest in anadromous populations and lowest in those isolated above waterfalls with stream-resident forms intermediate between the two. Anadromous and isolated populations were genetically divergent from each other while no genetic differentiation was detectable between sympatric anadromous and stream-resident forms. Population structure was stable over 25 years, hierarchically organized and conformed to an isolation-by-distance pattern, but stream-isolated forms often deviated from these patterns. Gene flow occurred primarily among Yukon North Slope populations and between sympatric anadromous and resident forms. These results were sex-dependent to some extent, but were influenced more by reproductive status and life history. Our study provides novel insights into the life history, population demographic and habitat variables that shape the distribution of genetic variation and population structure in Arctic fluvial habitats while providing a spatial context for management and conservation.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Dataset · Consensus signal: Dataset
Teacher disagreement score0.609
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.001
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 head0.021
GPT teacher head0.225
Teacher spread0.204 · 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.

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

Citations0
Published2020
Admission routes1
Has abstractyes

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