Predicting sustained smouldering combustion in trembling aspen duff in Elk Island National Park, Canada
Bibliographic record
Abstract
Fire is one of the key disturbances affecting trembling aspen (Populus tremuloides Michx.) forest ecosystems within western Canadian wildlands, including Elk Island National Park in central Alberta, Canada. Although prescribed fire is a tool available to modify aspen forests, a clear understanding of its potential impact is necessary to successfully manage this disturbance. Undesirable social and ecological consequences of severe, deep-burning ground fires include smoke generation and impaired vegetation regrowth. Data on the duff moisture conditions under which ground or subsurface fires may ignite and spread in aspen forest duff layers are presented, as well as experimental test fire results. Different topographic positions, plant communities and seasonality were factored into the research design. The Duff Moisture Code (DMC) and Drought Code (DC) components of the Canadian Forest Fire Weather Index System were calculated and factors including duff moisture content, bulk density and inorganic content measured before ignition of experimental test fires. Probability of sustained smouldering combustion models were developed for the duff layer in the aspen forest fuel type in Elk Island National Park, with values of 27 for DMC and 300 for DC at the 50% probability level.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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 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".