Does frequent burning affect longleaf pine (<i>Pinus palustris</i>) bark thickness?
Bibliographic record
Abstract
Bark thickness plays a critical role in protecting a tree’s cambium from damage by fires. Does frequent surface fire stimulate bark production compared with a fire-suppressed control? Based on data from an ongoing, long-term prescribed fire study, we explored differences in bark thickness from 0 to 200 cm above the ground for longleaf pine ( Pinus palustris P. Mill.) trees on burned and control sites. Biennial prescribed fires, applied between 1973 (stand age = 14 years) and 2006 (stand age = 47 years), significantly reduced diameter at breast height. After adjusting for differences in diameter at breast height, we found no difference in bark thickness of trees from the burn and control sites, except for bark thickness measured at 0 cm aboveground where control sites had significantly thicker bark than burned sites. Results suggest that frequent burning did not stimulate bark production in longleaf pine.
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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".