What fraction of the genomic basis of local adaptation are we missing?
Bibliographic record
Abstract
How will species adapt to changing environments? To what extent does adaptation to previous conditions maintain the variation needed to adapt to future conditions? To answer these kinds of questions, we need to identify locally adaptive alleles and quantify their effects. Theory shows that the architecture of adaptation can depend upon the nature of mutation and on how ecology shapes the processes of migration and selection. Depending on this interplay, adaptation can be driven by few alleles of large effect or many alleles of small effect, but little is known about the relative prevalence of such architectures in nature. Unfortunately, our statistical methods are also biased: it is much easier to identify loci of large effect that contribute repeatedly across populations or species, while alleles of small effect are all but invisible to genomic analysis. There is, therefore, a gap between the total amount of locally adaptive variation and that which is explained by genomic studies. To quantify this missing local adaptation, future studies require a deep integration of genomic and phenotypic analyses.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.003 | 0.008 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".