Best practices for genetic indicator estimation from DNA-based data
Bibliographic record
Abstract
The 2022 Kunming-Montreal Global Biodiversity Framework (GBF) of the Convention on Biological Diversity 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, including an effective population size above 500 (Ne>500) as an indicator for a healthy evolutionary status and adaptive potential.Numerous DNA datasets have recently been published due to increasingly cost-effective DNA sequencing technologies and enhanced collaborations between researchers and conservation practitioners. These datasets allow estimating GD within and across species. However, Ne estimates from DNA data are sensitive to species features including population spatial genetic structure and life history traits such as reproductive system or life span, sampling designs and estimation methods. The general aim of my PhD is to use both simulations and existing empirical datasets to perform sensitivity analyses for GD estimation. I will select DNA reference datasets representative of contrasted evolutionary histories, biological and data features to explore how their properties impact Ne estimates and will conduct simulations mimicking some of these features. This approach will eventually provide standardized workflows for robust sampling, molecular and statistical procedures, and best practices for reliable estimation of Ne to support conservation managers.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".