Economic evaluation of research to improve the Canadian forest fire danger rating system
Bibliographic record
Abstract
Canada is a largely forested country, and the economic, environmental, and social effects of the country's wildland fire management are of great importance from an industry and public policy perspective. Investment in research can improve the efficiency of wildland fire management and has an important role in the decision-making process. There is a long history of research investment in Canada related to wildland fire management, including the development of the Canadian Forest Fire Danger Rating System (CFFDRS). To demonstrate the range of net benefits of the CFFDRS to Canadian society, a cost-benefit study was conducted on research related to enhancing the current system. The benefits of research were measured as the difference in economic returns with additional investment in research, primarily achieved through reduction in damages to timber resources and savings in suppression expenditure (the “with-research scenario”) and those that would have resulted with no changes to the current CFFDRS (the “without-research scenario”). A triangular probability distribution was used to address uncertainty and the results indicated high levels of net economic benefit if the CFFDRS were to be enhanced by additional research investment, with “most likely” estimates of net present value ranging from $30 million to $1.5 billion ($Cdn).
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 | 0.083 | 0.156 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".