Genetic structure, diversity, and inbreeding of eastern white pine under different management conditions
Bibliographic record
Abstract
Resource sustainability requires a thorough understanding of the influence of forest management programs on the conservation of genetic diversity in tree populations. To observe how differences in forest structure affect the genetic structure of eastern white pine ( Pinus strobus L.), we evaluated six eastern white pine sites across the 234 000 acre (1 acre = 0.4046856 ha) Menominee Indian Reservation in northeastern Wisconsin (45°00′N, 88°45′W). The six sites sampled for nuclear and chloroplast DNA microsatellite markers were of contrasting densities and managed by different management systems: shelterwood, pine release, plantation, and old growth. Three of the sites had natural regeneration, which was also sampled. Mean values of spatial genetic autocorrelation were positive in all mature populations and variable; the strongest spatial structuring of genes occurred in the least disturbed old-growth site (I – E(I) = 0.031). Genetic structuring at the historical old-growth site fit the isolation-by-distance model for a neighborhood size of 130 individuals. Significant inbreeding occurred in five populations, but the seedling or sapling populations as a group (f = 0.088) are significantly less inbred than the local mature populations (f = 0.197). The increase in heterozygosity between generations was attributed to harvesting having reduced the spatial genetic structure of the mature trees.
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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 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".