Contrasting evolutionary forces driving population structure at expressed sequence tag polymorphisms, allozymes and quantitative traits in white spruce
Bibliographic record
Abstract
Patterns of variation in quantitative characters and genetic markers were compared among six regional populations of white spruce [Picea glauca (Moench) Voss]. Although some phenotypic characters were correlated with latitude (r = 0.791), longitude (r = -0.796) and precipitation during the growing season (r = 0.789), variability at genetic markers was not correlated with geographical or bioclimatic variables, and followed neutral expectations. Estimates of genetic diversity and population differentiation for 14 allozymes (translated regions of coding genes) were essentially indistinguishable from those observed for 11 expressed sequence tag polymorphisms (ESTPs) from untranslated regions of coding genes. Variation among populations for quantitative traits such as eighth year height (Q(ST) = 0.082), thirteenth year height (Q(ST) = 0.069), total wood density (Q(ST) = 0.102) and date of budset (Q(ST) = 0.246), was greater than for allozymes (G(ST) = 0.014) and ESTPs (G(ST) = 0.019). These trends suggest a strong adaptive response in quantitative traits, contrasting to allozymes and ESTPs where no selective response could be detected and where populations appeared to be essentially in a migration-drift equilibrium.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.000 | 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".