Relationships between understory vegetation and hybrid poplar growth and size in an operational plantation
Bibliographic record
Abstract
The effect of varying composition and abundance of neighbouring herbaceous vegetation on tree growth in commercial hybrid poplar plantations remains unclear. We evaluated relationships between tree stem volume, volume increment, and the abundance of various growth forms (perennial grasses, legumes, and perennial forbs excluding legumes) adjacent to the tree base in four-year-old plantation trees under conditions protected from ungulate herbivory. Associations between tree metrics and the understory revealed that perennial grasses consistently reduced tree size and current annual volume growth, and the negative effects of grasses were made worse by increasing legume cover. Under little to no perennial grass cover however, the presence of legumes had a positive impact on tree size and growth, suggesting net facilitative versus competitive effects. The pesence of forbs other than legumes led to no apparent competitive effects after four years, and may instead signify the absence of more competitive understory vegetation, particularly grasses. While final modelled relationships showed that the understory accounted for a limited amount of variance in tree volume (R2=0.10) and volume increment (R2=0.09) in four-year-old trees, these results nevertheless have implications for the development of silvicultural prescriptions targeting understory vegetation in order to maximize commercial hybrid poplar plantation yields.
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 | 0.001 |
| 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".