Modeling <i>Pinus strobus</i> mortality following prescribed fire in Quetico Provincial Park, northwestern Ontario
Bibliographic record
Abstract
In forest ecosystems where ecologically beneficial fire impacts are promoted through the use of prescribed fire, predictive models of fire effects, such as tree mortality, are essential for assessing the ecological consequences of fire management options. Impact of tree size, fire intensity, and fuel characteristics on postfire eastern white pine (Pinus strobus L.) mortality was evaluated 10 months following a prescribed fire in Quetico Provincial Park, northwestern Ontario. A logistic regression model was developed to predict white pine mortality following an intense surface fire (1200 kW/m). Overall fire-caused mortality was relatively low (17.0%). Probability of mortality increased with increasing height of stem blackening, a surrogate measure of fire intensity, and decreased with increasing tree diameter at 1.3 m (DBH). White pine with [Formula: see text]20 cm DBH were found to be highly resistant to intense surface fire. The model can be used to predict postfire mortality in white pine stands with a mixedwood understory in the Great Lakes St. Lawrence forest region of Ontario.
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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".