Linking adaptation, delimitation of evolutionarily significant units (ESUs), and gene function: a case study using hemlock looper ecotypes
Bibliographic record
Abstract
Developing genetic markers for the identification of recently diverged groups, such as ecotypes or species complexes, remains difficult due to challenges with incomplete lineage sorting, hybridization and introgression. Genome‐wide scans of single nucleotide polymorphisms (SNPs) have proven useful for inferring patterns of genetic differentiation at the population level. In combination with a new analytical technique, the discriminant analysis of principal components (DAPC), and within the framework of iterative taxonomy, it may also be possible to extract a combination of SNPs as markers for the delimitation of closely related groups. In addition, since DAPC identifies the loci contributing the most to group clustering, it may be possible to link putative biological function to differences that define group boundaries. We tested this technique on two ecotypes of the hemlock looper ( Lambdina fiscellaria ), which differ in terms of number of larval stadia, developmental rate and fecundity. It was possible to separately cluster the two ecotypes with 95% correct assignment using 27 SNPs. We also determined that a storage hexamerin carried eight of these SNPs, including the two highest contributing loci, of which the top contributor was nonsynonymous. Other studies have found this protein to be highly expressed just before metamorphosis, pointing to a possible connection between its role in clustering ecotypes and its biological function. These SNP markers can now be further developed for high throughput delimitation of individuals of unknown ecotype identity.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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".