Is Competition for Soil Resources by Carex pensylvanica Restricting Tree Seedling Growth in a Temperate Northern Hardwood Forest?
Bibliographic record
Abstract
In response to ecological disturbances, sedge species like Carex pensylvanica form dense monocultures on the forest floor. These “sedge mats” have been shown to severely inhibit plant growth, and limit understory species diversity. In recent years, concern has grown that they may also be restricting the regeneration of economically valuable tree species like sugar maple through belowground competition. To determine if and how Carex pensylvanica may be impacting tree seedling growth through belowground competition I located and exclosed 44 tree seedlings in areas of representatively dense sedge at two forest sites at the Queen’s University Biological Station. I removed sedge from half the plots, and measured soil resource availability and seedling growth response at all plots throughout the growing season. I predicted that sedge would negatively impact the growth of tree seedlings by decreasing the availability of soil resources. I found that the presence of sedge did not affect seedling growth over one growing season, but that it did impact soil resource availability by increasing the availability of surface soil moisture and decreasing the availability of soil nitrate, changes which may have implications for seedling growth beyond the single growing season studied. My results were also site specific, indicating that location is important when managing sedge impact on tree regeneration. Understanding the impact of sedge on tree seedling regeneration is important for predicting changes in the trajectory of forest communities and for informing the management of economically valuable species like sugar maple.
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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) | 0.001 | 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".