Wildfire mitigation strategies affect soil enzyme activity and soil organic carbon in loblolly pine (<i>Pinus taeda</i>) forests
Bibliographic record
Abstract
We quantified the effects of three wildfire hazard reduction treatments (prescribed fire, thinning from below, and the combination of fire and thinning), and passive management (control) on mineral soil organic C, and enzyme activity in loblolly pine (Pinus taeda L.) forests on the Piedmont of South Carolina. Soil organic C was reduced by thinning, either alone or with prescribed fire, and this effect persisted through the fourth post-treatment year. Fire also resulted in reduced soil organic C, but not until several years after treatment. Soil C/N ratio initially increased after fire, either alone or with thinning, but this difference did not persist. The activities of three soil enzymes (acid phosphatase, chitinase, and phenol oxidase) in the upper mineral soil were quantified as measures of microbial activity. During the fourth post-treatment year we observed significant stimulation of all three enzyme systems as a result of thinning or thinning and burning. Although the patterns of variation in acid phosphatase and chitinase activity among treatments were similar during the first and fourth post-treatment years, the first-year treatment effects were not statistically significant. Given the management objective of utilizing these stands for timber production, the increased potential for rapid nutrient turnover offered by thinning gives this approach advantages over prescribed fire; however, management for maximum long-term storage of soil C may be better facilitated by prescribed fire.
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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) | 0.000 | 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".