Bibliographic record
Abstract
Forests play an important role for sustaining the natural environment of human living. Forest fires not only destroy natural environment and ecological equivalence, but also threaten security of life and wealth to people. This paper presents applications of Spatio-temporal Data Mining and Knowledge Discovering (STDMKD) for forest fire prevention. The special attention of the research is paid to the spatio-temporal forecasting of forest fires because of the importance of prediction for the fire prevention. It is also due to the fact that most existing spatio-temporal forecasting methods cannot handle the dynamic development of forest fires over space. An improved spatio-temporal integrated forecasting framework – ISTIFF is proposed. The method and algorithm of ISTIFF are presented, which are illustrated by a case study of forest fire area predication in Canada. Comparative analysis of ISTIFF with other methods is implemented, which shows its high accuracy in short-term prediction. Based upon the forecasting result, more intelligent strategies of fire prevention and extinguishments can be delivered to decision makers in fireproofing. 1. INTRODCUTION Forests play an important role for sustaining the natural environment people live in. Because forest fires are among the best dangers for forest prevention, it is not a surprise to see increasing expenditures for forest fire control. Even so, millions of hectares of forests are still destroyed by fires every year
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.203 | 0.116 |
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".