Disturbance-based restoration for recovery of pitch pine (Pinus rigida) in the Thousand Islands Ecosystem: a comparison of prescribed fire and mechanical disturbance on seedling regeneration
Bibliographic record
Abstract
Disturbance plays an important role in maintaining forests worldwide. Many natural disturbance regimes, especially wildfire, have been modified, which can lead to the loss of disturbance adapted forest communities. Pinus rigida (pitch pine) is strongly associated with wildfire in the core of its range, however the association becomes less certain towards the species’ range margins. I tested the efficiency of a prescribed fire and mechanical disturbance treatment on increasing P. rigida seedlings using a Before-After Control-Impact (BACI) design at two sites at the northeast range margin of P. rigida in the Thousand Islands Ecosystem, Canada. Fire had the greatest effect on regenerating P. rigida seedlings and mechanical treatments were ineffective. My results suggest that the use of prescribed fire is the best approach to increase P. rigida seedlings in the Thousand Islands Ecosystem, possibly because these populations are exhibiting phenotypic plasticity in traits that favour conditions created by fire.
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".