Effects of fire severity and season of burn on Betula glandulosa growth dynamics
Bibliographic record
Abstract
Betula glandulosa survives over a wide range of North American fire regimes by resprouting from the rhizome. Over-winter root carbohydrate reserves are important to sprout production and growth in the following spring. Nursery and field experiments were conducted to examine the effects of seasonal clipping and fire severity (lethal heat applied to different soil depths) on B. glandulosa sprouting and growth, and seasonal burning and clipping on over-winter root carbohydrate storage. Low fire severity increased sprout numbers, and low fire severity in spring caused a large increase in height growth and above-ground biomass production over a 2-year period. Mortality rates increased sharply with higher levels of fire severity. Over-winter total non-structural carbohydrate (TNC) concentrations in roots were significantly higher in plants burned immediately after leaf-flush than in mid-summer burned plants. None of the other seasonal burning or clipping treatments significantly influenced over-winter root TNC. Post-disturbance sprout growth reflected over-winter root TNC levels. B. glandulosa survives a wide range of fire frequencies by growing in plant communities that are most likely to burn in spring or autumn, and seldom burn in summer. This provides the greatest opportunity for maximum over-winter TNC storage. As well, high soil moisture after snowmelt ensures spring fires are almost always of low severity, which promotes increased sprout production, height growth and above-ground biomass.
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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".