Fire Control as a Simple Means of Promoting Tropical Forest Restoration
Bibliographic record
Abstract
Tropical deforestation is occurring at an alarming rate. The loss of these forests contributes significantly to total global carbon dioxide emissions and accelerating rates of climate change; moreover, many deforested lands lose fertility and are abandoned. Demands to protect biodiversity and reverse climate change call for efforts to reforest such lands, and one method is through fire control, as fire suppresses tree regeneration. Unfortunately, the success of fire control is often not known for tropical regions because research efforts must span decades. We compared above ground biomass in two plots of regenerating forest that were protected from fire for 12 and 32 years in Kibale National Park, Uganda. Tree biomass of the plots was substantial, and while the biomass of the 12- and 32-year plots did not differ significantly, the 12-year plot had a higher biomass in the small diameter classes in comparison to the 32-year plot. Twenty-four tree species were growing in 12-year plot, while 46 grew in the 32-year plot. We conclude that fire exclusion is a promising approach for tropical forest restoration, and we demonstrate that it is cost-effective relative to programs that plant tree seedlings.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.002 | 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".