MétaCan
Menu
Back to cohort
Record W6929547087 · doi:10.5061/dryad.67n79r2

Data from: Landscape, colonization and life history: their effects on genetic diversity in four sympatric species inhabiting a dendritic system

2019· dataset· en· W6929547087 on OpenAlexaboutno aff

Bibliographic record

VenueDRYAD · 2019
Typedataset
Languageen
FieldMedicine
TopicHIV/AIDS drug development and treatment
Canadian institutionsnot available
Fundersnot available
KeywordsColonizationSympatric speciationGenetic diversityMetapopulationGene flowColonisationGenetic structureBiological dispersal

Abstract

fetched live from OpenAlex

To what degree are patterns of genetic structure in fragmented systems the result of contemporary landscape vs. history? We examined the distribution of genetic diversity as a function of colonization history and contemporary landscape in four fish species inhabiting a hierarchically fragmented, unaltered system, the Kogaluk drainage (Labrador): lake trout, longnose sucker, round whitefish, and lake chub. The footprint of colonization history was still observable in the three species where this issue was examined regardless of the generations since their arrival. ABC analyses suggest colonization took place from the southwest. The species exhibit similar diversity patterns despite different Nes and generation intervals. Contemporary gene flow was largely negligible except for gene flow from a centrally located lake. These results suggest landscape has driven colonization history, which still has influence on genetic structuring. The species are widespread. Understanding how they behave in the pristine Kogaluk provides a baseline against which to evaluate how other anthropogenically perturbed systems are performing. Improved understanding of historical and contemporary processes is required to fully explain diversity patterns in complex metapopulations

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: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Dataset · Consensus signal: Dataset
Teacher disagreement score0.027
Threshold uncertainty score0.071

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.003
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0210.013

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 head0.046
GPT teacher head0.236
Teacher spread0.190 · 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

Citations0
Published2019
Admission routes1
Has abstractyes

Explore more

Same venueDRYADSame topicHIV/AIDS drug development and treatmentFrench-language works237,207