Effects of pollen availability on pollen immigration and pollen donor diversity in riparian dioecious trees (<i>Salix arbutifolia</i>)
Bibliographic record
Abstract
Reduced pollen availability in a fragmented population may affect pollen immigration and diversity of pollen donors in the population. To test this prediction, tree locations, tree sizes, and nuclear microsatellite genotypes were determined for 182 offspring, their 8 mothers, and 194 males of a wind-pollinated, dioecious Salix arbutifolia Pallas population along a river isolated over 4 km from other populations. The effects of the distance between the mother and male trees and the size of the male trees on pollen dispersal as well as fractional paternity allocation for the offspring and the number of unsampled males were estimated simultaneously. Based on the estimated parameters of pollen dispersal, the availability of pollen from the males along the river was obtained for the individual mother trees. The pollen availability was not correlated with the proportion of offspring sired by immigrating pollen from unsampled males (8%–50%), but was positively correlated with the effective number of pollen donors in sampled males (1–8). The results suggest that the reduced pollen donor diversity owing to low pollen availability increases the fraction of full-sibs in offspring and subsequent reduction in genetic diversity, but that pollen immigration compensates the offspring for the reduced genetic diversity.
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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 | 0.000 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".