Clonal and spatial genetic structures of aspen (<i>Populus tremuloides</i> Michx.)
Bibliographic record
Abstract
To portray aspen clonal and spatial genetic structures, we mapped and genotyped trees in two 1-ha plots, each containing three aspen cohorts originating from fire or subsequent secondary disturbances. We used four microsatellite loci to identify aspen clones and increment core analysis to determine tree age. Clonal dimensions were measured by the maximum distance between two ramets and the number of ramets per genet. Standard normal deviate (SND) was used to assess the spatial distribution of aspen genets and cohorts, and multivariate spatial genetic autocorrelations to assess the spatial distribution of aspen genetic variation. Most aspen genets consisted of only one ramet (> 75%). Median clonal dimensions were 19 and 29 m (maxima: 104 and 72 m in the two plots). No segregation was observed between clones. Aspen cohorts were spatially segregated but trees were spatially aggregated within old and medium-aged cohorts. In contrast, trees were more randomly distributed within the youngest cohorts. This coincided with a spatial genetic autocorrelation at small scales (up to 30 m) in the older cohorts and a more random genetic distribution in the youngest ones. Our results suggest that aspen spatial genetic structuring reflects the spatial patterns produced by the regeneration of discrete cohorts at different stages of succession. Vegetative reproduction leads to aspen genetic spatial structuring at small scales (few metres) until midsuccession. However, as the stand gets older, the spatial distribution of aspen trees and genetic structure evolve from a structured pattern to a more random one under a gap disturbances regime.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.001 |
| 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".