[Change trends of summer fire danger in great Xing' an Mountains forest region of Heilongjiang Province, Northeast China under climate change].
Bibliographic record
Abstract
By using Delta and WGEN downscaling methods and Canadian Forest Fire Weather Index, this paper analyzed the variation characteristics of summer fire in Great Xing' an Mountains forest region of Heilongjiang Province in 1966-2010, estimated the change trends of the summer fire danger in 2010-2099, compared the differences of the forest fire in summer, spring, and autumn, and proposed the prevention and control strategies of the summer fire based on the fire environment. Under the background of climate warming, the summer forest fire in the region in 2000-2010 showed a high incidence trend. In foreseeable future, the summer forest fire across the region in 2010-2099, as compared to that in the baseline period 1961-1990, would be increased by 34%, and the increment would be obviously greater than that of spring and autumn fire. Relative to that in 1961-1990, the summer fire in 2010-2099 under both SRES A2a and SRES B2a scenarios would have an increasing trend, and, with the lapse of time, the trend would be more evident, and the area with high summer fire would become wider and wider. Under the scenario of SRES A2a, the summer fire by the end of the 21st century would be doubled, as compared to that in 1961-1990, and the area with high summer fire would be across the region. In the characteristics of fire source, attributes of forest fuel, and fire weather conditions, the summer forest fire was different from the spring and autumn forest fire, and thus, the management of fire source and forest fuel load as well as the forest fire forecast (mid-long term forecast in particular) in the region should be strengthened to control the summer 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 | 0.000 | 0.000 |
| 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 | 0.000 | 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".