Compositional, cover, and diversity changes after prescribed fire in a mature eastern white pine forest
Bibliographic record
Abstract
Eastern white pine ( Pinus strobus L.) forests are considered fire dependent, but little is known about the role of low-intensity fires. We conducted four prescribed burns to examine understory effects. The vegetation was sampled one year before and two consecutive years after the burns. Understory metrics were calculated for the vernal (May–June) and aestival (mid-August) assemblages. The fires resulted in a cumulative mortality rate in the sapling layer of 64%. During the first year, the burns had a neutral or repressive effect on the understory. However, cover, richness, and species density increased significantly for both assemblages during the second year; however, the relative change was greater for the vernal assemblage. The fires also led to greater compositional turnover than in the controls. The large increases in late-season cover were primarily from reduced competition and enhanced regeneration. The fires created a brief window of invasion opportunity, which was minimally captured by annuals and exotics. Ordination indicated different short-term successional pathways for the vernal (diverging) and aestival (converging) assemblages. This difference by time period was consistent with similarity measures. The study suggests that low intensity fires play a vital role in understory diversity and structure in white pine forests.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 teacher head, 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".