Bibliographic record
Abstract
Climate warming has already made great impact on forest fires. Based o n the daily meteorological data (temperature, precipitation, relative humidity, and wind speed), and by using the Canadian Fire Weather Index calculation formul a, the daily forest fire weather indices (FWIs) of Yunnan Province in 1957-2007 were calculated, and through the statistical analysis of FWIs, the forest fire trends in this province over the past 50 years were studied. In the past 50 year s, the forest fire season in Yunnan Province was from previous year November to current year June, lasting 8 months. Fire data (fire numbers, burned area, and b urned forest area) had significant relationships with fire weather indices. The average daily duff moisture code (DMC) in whole fire season and the seasonal sev erity rating (SSR) were the good indices to evaluate the fire danger conditions among different fire seasons. The forest fire danger in Yunnan Province in 1957 -2007 showed two change trends. One showed a clear cyclical ch ange and a weak upward trend, i.e., the fire danger conditions in 1991-200 7 wa s slightly severe r than that in 1961-1990; and the another was that the fluctua tion of forest fi re danger conditions among different fire seasons decreased in 1991-2007, and the number of abnormal severe fire seasons was less than that in 1961-1990.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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 teacher head, 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".