Сравнительный анализ динамики потерь лесного покрова Канады и России с 1985 по 2011 гг.
Bibliographic record
Abstract
The subject of the study is the analysis and assessment of losses of forest-covered area from wildfi res in Russia and Canada in the period from 1985 to 2011. The aim of the work is to predict the prospects of using the Canadian fi re control system in Russia. Climate change causes an increase in the number of forest fi res. Different models are being developed to deal with fi res. The model is able to estimate the carbon dioxide emissions as a result of fi re. Data from satellite forest monitoring in Canada and Russia have been used as a source of information on forest fi res. These data made it possible to track the dynamics of losses of forest area for the period from 1985 to 2011. Data on losses of forests were grouped due to: forest fi res, logging, the creation of forest infrastructure and group of unidentifi ed reasons. The concept of «forest fi re control zones» is introduced in Russia. This concept is established in the zone of aviation works in the woods located in remote and remote territories. The new approach to forest fi re management can be seen as a borrowing from one of the key elements of Canada’s fi re management system. The loss of forest cover area from fi res in Canada and Russia from 1985 to 2011 was estimated to predict the prospects for the use of the Canadian fi re control system in Russia. The main share (about 50 %) of loss of forested area in Canada relates to forest fi res. The total area of forest fi res in Canada is higher than in Russia. The comparative analysis of forest mountain ability of Canada and Russia showed that the introduction of elements of the Canadian fi re control system in the short term can lead to an increase in the area of forest fi res.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.027 | 0.015 |
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; both teacher heads agree on what is shown here.
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".