Integrating forest fuels and land cover data for improved estimation of fuel consumption and carbon emissions from boreal fires
Bibliographic record
Abstract
Estimating carbon emissions from wildland fires is complicated by the large variation in both forest fuels and burning conditions across Canada’s boreal forest. The potential for using spatial fuel maps to improve wildland fire carbon emission estimates in Canada’s National Forest Carbon Monitoring, Accounting and Reporting System (NFCMARS) was evaluated for select wildfires (representing a transect across western Canada) occurring in 2003 and 2004 at four study areas in western Canada. Area-normalised emission rates and total emissions differed by fuels data source, mainly as a function of the treatment of open fuels in the higher resolution spatial fuel models. The use of spatial data to refine the selection of stand types that probably burned and the use of fire weather conditions specific to the fire increased the precision of total carbon emission estimates, relative to computational procedures used by Canada’s NFCMARS. Estimates of total emissions from the NFCMARS were consistent with the regional and national data sources following the spatial approach, suggesting the two approaches had equivalent accuracies. Though it cannot be said with certainty that the inclusion of this detailed information improved accuracy, the spatial approach offers the promise or potential for more accurate results, pending more consistent fuel maps, especially at finer scales.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".