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Record W6911121688 · doi:10.5061/dryad.jp43fg6

Data from: Invasive species and postglacial colonization: their effects on the genetic diversity of a Patagonian fish

2019· dataset· en· W6911121688 on OpenAlexaff

Bibliographic record

VenueData Archiving and Networked Services (DANS) · 2019
Typedataset
Languageen
Field
Topic
Canadian institutionsDalhousie University
Fundersnot available
KeywordsGenetic diversitySpecies richnessRange (aeronautics)PopulationMicrosatelliteQuaternaryGenetic structureGenetic variation

Abstract

fetched live from OpenAlex

The present distribution of Patagonian species is the result of a complex history involving Quaternary refugial populations, Holocene range expansions, and demographic changes occurring during the Anthropocene. Invasive salmonids were introduced in Patagonia during the last century, occupying most rivers and lakes, preying on, and competing with native species, including the fish Galaxias platei. Here we used G. platei as a case study to understand how long-term (i.e. population differentiation during the Holocene) and short-term historical processes (salmonid introductions) affect genetic diversity. Using a suite of microsatellite markers, we found that the number of alleles is negatively correlated with presence of salmonids (short-term processes), with G. platei populations from lakes with salmonids exhibiting significantly lower genetic diversity than populations from lakes without salmonids. Simulations (100 years backwards) showed that this difference in genetic diversity can be explained by a 99% reduction in population size. Allelic richness and observed heterozygosities were also negatively correlated with the presence of salmonids, but also positively correlated with long-term processes linked to Quaternary glaciations. Our results show how different genetic parameters can help identify processes taking place at different scales and their importance in terms of 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 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)0.005
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: none
GenreCandidate signal: Dataset · Consensus signal: Dataset
Teacher disagreement score0.097
Threshold uncertainty score0.192

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.007
Science and technology studies0.0010.000
Scholarly communication0.0020.001
Open science0.0020.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0510.030

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 head0.043
GPT teacher head0.242
Teacher spread0.199 · 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
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
Published2019
Admission routes1
Has abstractyes

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