Evaluating fire management effectiveness with a burn probability model in Daxing’anling, China
Bibliographic record
Abstract
Fire is an important disturbance agent in the boreal forests of China. The aggressive fire suppression policy of China since 1988 has resulted in a large financial investment in support of fire brigade capabilities and the maintenance of fire management infrastructure. We developed a spatially explicit burn probability (BP) model to evaluate the effectiveness of improved fire management in Daxing’anling, China. The BP model can emulate the burn probability of the forest landscape by simulating daily wildfire occurrences, spread, and suppression for simulated years. Two scenarios were used for fire simulations in this study. The base scenario used the infrastructure data and parameters of fire suppression capability from the 1968–1987 period, and the intensive scenario used the data and parameters from the 1988–2012 period. The simulated annual burned areas for 1968–2012 showed a fluctuating trend similar to the historical fire records. Compared with the base scenario, the burn probability decreased by 73.6% under the intensive scenario, which suggests that improved fire management could significantly reduce the burn probability. This study shows that the BP model can model the effects of fire management activities on the forest landscape level and evaluate the effectiveness of fire management strategies or management measures.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".