Time since prior wildfire affects subsequent fire containment in black spruce
Bibliographic record
Abstract
In black spruce forests characterised by high-intensity crown fires, early detection and containment of fires while they are small is crucial for averting progression to fire intensities that exceed suppression capabilities. Fire behaviour conditions encountered during initial attack operations are a key determinant of containment success. Conditions will be controlled in part by stand structural characteristics that can be expected to vary as a fire-origin black spruce (Picea mariana (Mill.) B.S.P.) stand ages with increasing time-since-fire. In this study, the influence of time-since-fire on containment outcomes is assessed to explore whether or not prior wildfire exerts a negative feedback on subsequent fires in these ecosystems. Logistic regression analysis using point and polygon fire data for the province of Alberta, Canada, indicated the probability of a containment failure in black spruce increases with time-elapsed since the last fire. Other positive explanatory variables included the size of the fire at the initiation of firefighting and a relative rating of the expected rate of fire spread, the Initial Spread Index (ISI) of the Canadian Forest Fire Weather Index System. Legacy wildfires had a protective effect. When firefighting is initiated at fire sizes ≤1 ha, the probability of a containment failure is low during the initial 20–45 years of post-fire stand development, except under the most extreme fire weather conditions.
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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 | 0.001 | 0.002 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 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".