Key factors determining scales of burned areas in state Victoria (Australia) and province Alberta (Canada) during 1980-2019
Bibliographic record
Abstract
Regular wildfire supports balanced development of sclerophyll forests in Victoria (Australia), as well as, boreal forest in Alberta (Canada). Also, they are a major part of local Aboriginal culture in these regions and a means regulating ecological functions of flammable vegetation communities for improvement their productivity. Taking into consideration that burning is used as an effective tool for ecosystem management in Alberta and Victoria, it is relevant to assess impacts of fire practices on the environment and to find connections between fire spread and key factors determining scales and locations of burned areas. Basing on literary materials on fire practices, statistical data on wildfire cases occurring since 1980s, geospatial data on distribution of fire-prone plant communities’ locations, and on results of correlation analysis of fire cases with climatic, environmental, infrastructural, and social factors author reveals the following patterns: fires frequency depends on the landscape features; an increase in number of fire occurrences correlates with increase of dry periods duration (number of days); human settlements, where Aboriginal population reaches 50%, are subject to fires more frequent; the risk to the environment and settlements damage on small populated rural areas is higher, than on densely populated suburban and urban places. Reduction of out-of-control wildfire risk can be achieved through fire management practices directed to wildlife and biodiversity protection, considering these patterns.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".