Predicting immediate and delayed fire-induced mortality of Pinus monticola and Pseudotsuga menziesii saplings using a pyro-ecophysiology fire severity approach
Bibliographic record
Abstract
Background Although fires can cause tree mortality or reduce post-fire growth in trees of all ages, and models exist that predict fire-induced mortality in mature trees, the development of predictive models of how fires impact younger trees has received less attention. Aims To assess whether inclusion of fire behaviour metrics alongside pre- and post-fire sapling morphological traits improve the prediction of fire-induced tree mortality as compared to existing models. Methods In this study, we subjected Pseudotsuga menziesii (Mirb.) Franco var. glauca (Beissn.) and Pinus monticola var. minima Lemmon saplings to increasing levels of fire intensity and evaluated models to predict immediate and delayed post-fire mortality. Key results For Pinus monticola, the optimal model relied on the post-fire crown volume scorched, while for Pseudotsuga menziesii the optimal model used flame height and fire radiative energy. We show that while Pinus monticola saplings exhibit immediate fire-induced mortality, Pseudotsuga menziesii saplings are prone to delayed fire-induced mortality. Implications Even in younger trees, crown volume scorched and related metrics remain consistent predictors of fire-induced tree mortality. Future studies should track mortality over extended periods to ensure that developed models better represent delayed fire-induced tree mortality.
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) | 0.001 | 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) | 0.000 | 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".