Spatial and Temporal Variability of Forest Floor Moisture Characteristics and Their Influence on Wildfires in Western Siberia over 2016–2021
Bibliographic record
Abstract
Abstract The spatial and temporal variability of forest floor moisture characteristics is analyzed on the basis of the Canadian Forest Fire Weather Indices (CFFWIS) for the territory of Western Siberia (45°–75° N, 60°–90° E) over 2016–2021 for the first time. The floor moisture effect on the number of wildfires (hotspots) during the warm season (March–October) is assessed. The results are given for different natural zones. Statistically significant correlations are found between hotspots and floor moisture at a depth of 7 cm only in certain spring and summer months (correlation coefficient is up to 0.54). The strongest effect (correlation coefficient is up to 0.60) on wildfires is observed for floor moisture at a depth of 1.2 cm in the south of Western Siberia in April. Thus, we can conclude that the forest floor moisture is an important parameter in description of conditions for fire initiation and development. However, its effect on the behavior of wildfires requires additional studies with accounting for meteorological and atmospheric conditions. The results can be used for forecasting the potential fire danger.
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 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 | 0.000 |
| 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".