Risk management of forest conflagration on Russian Federation territory
Bibliographic record
Abstract
Abstract: \n At present in Russian Federation the Federal Special Program “Risk Reduction” is carried out. They entrust RAS institutes a part of investigations in frame of this program. At Keldysh Institute of Applied Mathematics RAS they carries out a part of the work concerned with creating the experiment stand of National scientific system to monitor perilous events, phenomena and processes in nature, man-caused and social spheres. In particular a system analysis of different types’ catastrophes and disasters is carried out. The present work is devoted to the actual for Russia problem of forest conflagrations.\nAt last years in Russia forest conflagrations made up a great part of emergency situations caused by perilous processes in nature. In the work we studied a natural conflagration statistics for 2005-2007 and carried out an analysis of natural conflagration extinguishing system in Russia.\nIn present work we focus a main attention to statistical data on forest conflagrations at federal and regional levels, to organization of national system of conflagration extinguishing in Canada, and to dynamics of separate conflagrations.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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".