Recurring surface fires cause soil degradation of forest land: A simulation experiment with the <scp>EFIMOD</scp> model
Bibliographic record
Abstract
Abstract Renewal of pine forests is ecologically dependent on fires, but if fires become too frequent, they can disrupt the equilibrium and sustainability of these ecosystems. Field studies of the effects of fire are challenging because of the heterogeneity of forest ecosystems and because of the heterogeneous effect of fire on recovery of vegetation. As an alternative to complex field studies, mathematical models can be used as a tool to assess the complex dynamics of natural ecosystems as they recover after fire. The aim of this study was to apply the ecosystem model EFIMOD to analyse the effect of surface fires on soil degradation and its feedback on tree productivity in Scots pine forests on different soil types in Russia: Haplic Podzols in the Leningrad region and Psamment Entisols of the fragmented steppe in the Samara region. Simulation of the cumulative effects of fire cycles over 140 years showed that one fire did not affect growing stock but decreased soil organic matter by about 10% at both sites, and that three fires reduced the growing stock by 30% on the Haplic Podzols and 9% on the Psamment Entisols and decreased soil organic matter by about 30% on both sites. Forest fires led to the loss of soil carbon (C), as well as nitrogen (N), which is a principal limiting factor in forest ecosystems of boreal and temperate ecozones. The effect of repeated fire cycles on land degradation is similar to that of soil erosion, through the loss of soil C and N.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".