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Record W6929573298 · doi:10.5061/dryad.8c4c359

Data from: Estimated six percent loss of genetic variation in wild populations since the industrial revolution

2019· dataset· en· W6929573298 on OpenAlexaff

Bibliographic record

VenueData Archiving and Networked Services (DANS) · 2019
Typedataset
Languageen
Field
Topic
Canadian institutionsMcGill UniversityQueen's University
Fundersnot available
KeywordsGenetic variationGenetic variabilityGenetic diversityVariation (astronomy)PopulationAdaptation (eye)

Abstract

fetched live from OpenAlex

Genetic variation is fundamental to population fitness and adaptation to environmental change. Human activities are driving declines in many wild populations and could have similar effects on genetic variation. Despite the importance of estimating such declines, no global estimate of the magnitude of ongoing genetic variation loss has been conducted across species. By combining studies that quantified recent changes in genetic variation across a mean of 27 generations for 91 species, we conservatively estimate a 5.4-6.5% decline in within-population genetic diversity of wild organisms since the industrial revolution. This loss has been most severe for island species, which show a 30% average decline. We identified taxonomic and geographic gaps in temporal studies that must be urgently addressed. Our results are consistent with single time-point meta-analyses, which indicated that genetic variation is likely declining. However, our results represent the first confirmation of a global decline, and provide an estimate of the magnitude of the genetic variation lost from wild populations.

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)0.002
metaresearch head score (Gemma)0.011
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 score0.083
Threshold uncertainty score0.278

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.011
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0040.009
Science and technology studies0.0010.000
Scholarly communication0.0020.001
Open science0.0030.002
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0830.067

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 head0.115
GPT teacher head0.322
Teacher spread0.207 · 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
Published2019
Admission routes1
Has abstractyes

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