Data from: Does genome-wide variation and putatively adaptive variation identify the same set of distinct populations?
Bibliographic record
Abstract
Abstract Identifying which populations within species to prioritize for conservation is a major challenge: one question is whether to prioritize populations based on adaptive variation versus considering genome-wide genetic variation. Many authors have advocated focusing solely on adaptive variation due to its direct connection to selection, function, and adaptive capacity. However, there are many limitations in identifying and using adaptive genetic variation for conservation. Patterns of genome-wide genetic variation may be congruent with patterns of adaptive genetic variation, and genome-wide variation is much easier to measure. However, evidence for congruence is mixed. We gather genome-wide and putatively adaptive SNP data across 34 species of plants and animals from published outlier and association studies to test congruence. We ask whether putatively adaptive subsets of genome-wide SNPs identify the same distinctive populations (measured using the Shapley Value of distinctiveness) as genome-wide SNPs. We find that genome-wide and putatively adaptive SNPs generally but variably agree on population prioritizations. As expected, the level of agreement is predicted by the proportion of putatively adaptive SNPs, and the agreement is lower when there is more overall population genetic structure. Interestingly, across our datasets, putatively adaptive SNPs do as well or better at predicting genome-wide population prioritization than sized-matched random subsets of SNPs. Taken together, using genome-wide genetic variation for population prioritization may be a generally sound and cost-effective strategy for prioritizing populations in order to safeguard species-level genetic variation.
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 imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 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; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".