Genet diversity, genetic structure, and trait expression of trembling aspen (<i>Populus tremuloides</i> Michx.) after clear-cut harvesting and post-harvest soil treatments
Bibliographic record
Abstract
Trembling aspen is a fast growing, broadly distributed tree in North America that reproduces by root suckers (clonally) and by seeds (sexually). Since timber harvesting can affect both reproduction pathways by changing the soil properties, we assessed genet diversity, genetic structure, and phenotypic trait expression of trembling aspen populations after clear-cut harvesting in a long-term soil productivity study site in Michigan. A total of 323 stems in replicated plots having heavy soil compaction and surface organic matter (OM) removed, and the untreated controls, were analyzed at microsatellites and for phenotypic traits. Overall, higher spatial aggregation of ramets from the same genet, a greater spatial separation of different genets, and a very good phenotypic separation among genets was observed in the treatment plots. In contrast, for plots with spatially intermixed clones phenotypic separation was much lower. Genetic differentiation was high between individual plots, but soil compaction and OM removal had little effect on genetic differentiation and diversity. One treatment plot was strongly differentiated phenotypically, but not genetically, from all other plots. Consequently, microenvironmental and genetic differences should be considered when forest management effects on trait expressions and productivity are analyzed. Overall, heavy soil compaction and OM removal in fine-textured soil seemed to have restricted the spread of aspen clones.
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 | 0.000 | 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".