Manipulating aspen (<i>Populus tremuloides</i>) seedling size characteristics to improve initial establishment and growth on competitive sites
Bibliographic record
Abstract
Trembling aspen is an important component of the boreal mixedwood forest region and is a key component of numerous ecological functions. As a result, aspen has become a target species for forest restoration after industrial disturbance; however, aspen seedling afforestation research is still in its infancy as seedling and site characteristics and their interactions which drive establishment success in the field are poorly understood. This study examined the growth response of three aspen size classes across six sites differing in competition and soil moisture availability. With competing vegetation present, tall seedlings with lower root-to-shoot ratios had less height growth aboveground and reduced root egress (relative to total root mass) belowground, indicating that light was not the most limiting factor aboveground and that higher root-to-shoot ratios may aid in competition for rooting space. Differences between size classes were less pronounced on sites with no competition or sites with extreme conditions (high competition or low soil moisture availability) during outplanting. Reducing competing vegetation before outplanting improved height growth, particularly in seedlings with high root-to-shoot ratios. For aspen, planting short to medium-sized seedlings with higher root-to-shoot ratios offers a reasonable balance between cost and production of a stress-tolerant seedling.
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) | 0.001 | 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".