Commercial tree regeneration 6 years after high-intensity burns in a seasonally dry forest in Bolivia
Bibliographic record
Abstract
The effects of three site-preparation treatments (high-intensity burn, low-intensity burn, and mechanical cleaning with machetes and chainsaws) on the regeneration of commercial tree species, composition and structure of competing vegetation, and soil chemical and structural properties were evaluated in a seasonally dry forest in southeast Bolivia. Six years after controlled burns, the high-intensity burn treatment had the both the highest density and the tallest individuals of shade-intolerant commercial tree species. Competing vegetation was also less dominant in the high-intensity burn treatment relative to other treatments. However, high-intensity burns were not beneficial for commercial tree species with shade-tolerant or intermediate regeneration. Soil analyses revealed that certain changes in soil texture and soil chemistry (e.g., Ca and Mg concentrations and cation exchange capacity) caused by high-intensity burns persisted 6 years after the fires. These findings confirm that this suite of shade-intolerant commercial species requires very intense disturbances for their regeneration. However, several ecological, economic, and social barriers currently preclude the management-scale application of prescribed fire in Bolivia. More research is needed on cost-effective treatments to improve regeneration.
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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".