Biotic and physical drivers of fire in northwestern Patagonia
Bibliographic record
Abstract
Abstract Background Understanding the drivers of fire is frequently challenging because some of them interact and influence each other. In particular, vegetation type is a strong control of fire activity, but at the same time it responds to physical and human factors that also affect fire, so their effects are often confounded. We developed a 30 m resolution record of fire for northwestern Patagonia spanning 24 years (July 1998 - June 2022), and present an updated description of fire patterns and drivers. We analysed interannual variation in fire activity in relation to interannual climatic variation, and assessed how topography, precipitation, and human factors determine spatial patterns of fire either directly or by affecting the distribution of vegetation types along physical and human-influence gradients. Results We mapped 234 fires ≥ 10 ha that occurred between 1999 and 2022, which burned 5.77% of the burnable area. Both the annual burned area and the number of fires increased in warm and dry years. Spatially, burn probability decreased with elevation and increased with slope steepness, irrespective of vegetation type. Precipitation decreased burn probability, but this effect was evident only across vegetation types, not within them. Controlling for physical drivers, forests showed the lowest burn probability, and shrublands, the highest. Conclusions Interannual climatic variation strongly controls fire activity in northwestern Patagonia, which is higher in warmer and drier years. The climatic effect is also evident across space, with fire occurring mostly in areas of low elevation (high temperature) and low to intermediate precipitation. Spatially, the effect of topography on fire activity results from how it affects fuel conditions, and not from its effect on the distribution of vegetation types. Conversely, the effect of precipitation resulted mostly from the occurrence of vegetation types with contrasting fuel properties along the precipitation gradient: vegetation types with higher fine fuel amount and continuity and intrinsically lower fuel moisture occurred at low and intermediate precipitation. By quantifying the variation in burn probability among vegetation types while controlling for physical factors, we identified which vegetation types are intrinsically more or less flammable. This may help inform fuel management guidelines.
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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 | 0.001 | 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".