Multi-variable bias correction: application of forest fire risk in present and future climate in Sweden
Bibliographic record
Abstract
Abstract. As the risk of a forest fire is largely influenced by weather, evaluating its tendency under a changing climate becomes important for management and decision making. Currently, biases in climate models make it difficult to realistically estimate the future climate and consequent impact on fire risk. A distribution-based scaling (DBS) approach was developed as a post-processing tool that intends to correct systematic biases in climate modelling outputs. In this study, we used two projections, one driven by historical reanalysis (ERA40) and one from a global climate model (ECHAM5) for future projection, both having been dynamically downscaled by a regional climate model (RCA3). The effects of the post-processing tool on relative humidity and wind speed were studied in addition to the primary variables precipitation and temperature. Finally, the Canadian Fire Weather Index system was used to evaluate the influence of changing meteorological conditions on the moisture content in fuel layers and the fire-spread risk. The forest fire risk results using DBS are proven to better reflect risk using observations than that using raw climate outputs. For future periods, southern Sweden is likely to have a higher fire risk than today, whereas northern Sweden will have a lower risk of forest fire.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".