Cross‐species outlier detection reveals different evolutionary pressures between sister species
Bibliographic record
Abstract
Lodgepole pine (Pinus contorta var. latifolia) and jack pine (Pinus banksiana) hybridize in western Canada, an area of recent mountain pine beetle range expansion. Given the heterogeneity of the environment, and indications of local adaptation, there are many unknowns regarding the response of these forests to future outbreaks. To better understand this we aim to identify genetic regions that have adaptive potential. We used data collected on 472 single nucleotide polymorphism (SNP) loci from 576 tree samples collected across 13 lodgepole pine-dominated sites and four jack pine-dominated sites. We looked at the relationship of genetic diversity with the environment, and we identified candidate loci using both frequency-based (arlequin and bayescan) and correlation-based (matsam and bayenv) methods. We found contrasting relationships between environmental variation and genetic diversity for the species. While we identified a number of candidate outliers (34 in lodgepole pine, 25 in jack pine, and 43 interspecific loci), we did not find any loci in common between lodgepole and jack pine. Many of the outlier loci identified were correlated with environmental variation. Using rigorous criteria we have been able to identify potential outlier SNPs. We have also found evidence of contrasting environmental adaptations between lodgepole and jack pine which could have implications for beetle spread risk.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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; a candidate call from one teacher head, 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".