How can DNA legacy datasets contribute to developing best practices for CBD genetic diversity indicator estimation?
Bibliographic record
Abstract
The 2022 Kunming-Montreal Global Biodiversity Framework (GBF) of the Convention on Biological Diversity (CBD) recognized the protection of genetic diversity (GD) as a major objective for biodiversity conservation. To monitor GD for the GBF, genetic indicators have been proposed, requiring the estimation of the effective population size (Ne) which relates to a healthy evolutionary status and adaptive potential (Ne>500).Due to increasingly cost-effective DNA sequencing technologies and enhanced collaborations between researchers and conservation practitioners, an increasing number of DNA datasets are published. These datasets allow estimation of GD within and across species. However, Ne estimates from DNA data are sensitive to sampling designs and estimation methods applied, as well as to species features including population spatial genetic structure and life history traits such as reproductive system or life span. The best practices and workflows for Ne estimation need further development.DNA legacy datasets are large population genetic datasets of well-characterized species including DNA-data and metadata on census sizes, reproductive success and dispersal, sometimes with pedigrees. We present an approach to select and structure legacy datasets according to their features, resulting in a framework that will allow us to explore how contrasted evolutionary histories, biological characteristics and data features can impact Ne estimates. This framework will help us design simulation scenarios that mimic some of these features. It is intended to provide standardized workflows for reliable sampling and Ne estimating procedures to efficiently support conservation managers in their practice of assessing and reporting DNA-based GD indicators to meet management and policy requirements including those of the CBD.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.007 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; both teacher heads agree on what is shown here.
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".