Spatial and temporal opportunities for forest resilience promoted by burn severity attenuation across a productivity gradient in north western Patagonia
Bibliographic record
Abstract
Background Fire regimes in many biomass-rich ecosystems worldwide are dominated by high-severity fires. Many of these systems lack fire-resistant traits or post-fire regeneration strategies. Understanding under which environmental and weather conditions they experience less severe fire is crucial for maintaining their persistence in the landscape. Aims Understand the spatial and temporal conditions that allow burn severity attenuation across Patagonia’s productivity gradient. Methods We modelled burn severity as a function of topography, weather, vegetation and productivity. Key results Low severity was a rare phenomenon, affecting only 8% of the areas burned. The probability of burning with high severity followed a hump-shaped relationship with productivity. Low severity occurred in fires that burned under cool and wet summer conditions in areas with sparser fuels or in wetter and more productive environments but with discontinuous and wet fuels. Conclusions Across the regional gradient, ecosystems of intermediate productivity generally lack conditions for low burn severity. Temporally, low burn severity occurs in smaller fires burning in productive ecosystems during cool and wet summers. Implications Future climate scenarios of increasing aridity and temperature in the region will disfavour conditions for low burn severity, thus promoting fire-mediated transitions from forests to alternative states dominated by more fire-adapted flammable species (e.g. shrublands).
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".