Estimating direct carbon emissions from Canadian wildland fires1
Bibliographic record
Abstract
In support of Canada’s National Forest Carbon Monitoring, Accounting and Reporting System, a project was initiated to develop and test procedures for estimating direct carbon emissions from fires. The Canadian Wildland Fire Information System (CWFIS) provides the infrastructure for these procedures. Area burned and daily fire spread estimates are derived from satellite products. Spatially and temporally explicit indices of burning conditions for each fire are calculated by CWFIS using fire weather data. The Carbon Budget Model of the Canadian Forest Sector (CBM-CFS3) provides detailed forest type and leading species information, as well as pre-fire fuel load data. The Boreal Fire Effects Model calculates fuel consumption for different live biomass and dead organic matter pools in each burned cell according to fuel type, fuel load, burning conditions, and resulting fire behaviour. Carbon emissions are calculated from fuel consumption. CWFIS summarises the data in the form of disturbance matrices and provides spatially explicit estimates of area burned for national reporting. CBM-CFS3 integrates, at the national scale, these fire data with data on forest management and other disturbances. The methodology for estimating fire emissions was tested using a large-fire pilot study. A framework to implement the procedures at the national scale is described.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 teacher head, 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".