The interactive effects of surface-burn severity and canopy cover on conifer and broadleaf tree seedling ecophysiology
Bibliographic record
Abstract
Fire has an important role for regeneration of many boreal forest tree species, and this includes both wildfire and prescribed burning following clear-cutting. Depending on the severity, fire can have a variety of effects on above- and below-ground properties that impact tree seedling establishment. Very little is known about the impacts of ground fire severity on post-fire seedling performance, or how the effects of fire severity interact with those of canopy structure. We conducted a full-factorial experiment that manipulated surface-burn severity (no burn; light, medium, or heavy burn; or scarification) and canopy (closed forest or open clear-cut) to reveal their interactive effects on ecophysiological traits of establishing broadleaf and conifer seedlings in a Swedish boreal forest. Medium and heavy surface burns increased seedling growth, photosynthesis, respiration, and foliar N and P concentrations, and these effects were most apparent in open clear-cuts. Growth rates of all species responded similarly to surface-burn treatments, although photosynthesis, foliar P, and specific leaf area were more responsive to burning treatments for broadleaf species than for conifers. Our study demonstrates that the positive impacts of fire on tree seedling physiology are dependent on a minimum severity threshold and are more effective when combined with clear-cutting.
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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".