Experimental assessment of forest flammability after selective logging in the Brazilian Amazon
Bibliographic record
Abstract
Abstract Tropical forests, strongholds of biodiversity and carbon storage, face increasing threats from selective logging and fires. Selective logging disrupts forest structure, leaving canopy gaps where commercially valuable trees once stood, potentially increasing fire susceptibility through heating and drying understorey microclimates and altering fuel conditions. Here, we empirically examine the effects of selective logging on microclimate and flammability in the Brazilian Amazon. Using a controlled fire experiment during the first dry season post-harvest, we found that logging gaps were hotter and drier than surrounding forests, with larger gaps showing steeper temperature gradients. Leaf-litter moisture, a strong predictor of ignition, was modestly lower in gap centres. Despite this spatial variability in fuel moisture, the propensity of fuels to catch and sustain fires consistently increased as the dry season advanced, suggesting the selectively logged mosaic may be uniformly vulnerable to fire once exposed to ignition sources. These findings suggest that selective logging does not act alone in driving fire risk, with seasonal drying and ignition sources also contributing to increased vulnerability. These results highlight the importance of ignition suppression in post-logging management of forests that continue to hold substantial conservation value, including biodiversity and ecosystem services, as dry seasons intensify under climate change.
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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 | 0.000 | 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".