Measuring Initial Attack Suppression Effectiveness through Burn Probability
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Most wildfires in North America are quickly extinguished during initial attack (IA), the first phase of suppression. While rates of success are high, it is not clear how much IA suppression reduces annual fire risk across landscapes. This study introduces a method of estimating IA effectiveness by pairing burn probability (BP) analysis with containment probability calculations based on initial fire intensity, spread rate, and crew response time. The method was demonstrated on a study area in Kootenay National Park, Canada by comparing burn probabilities with and without modeled IA suppression. Results produced landscape-level analyses of three variables: burn probability, suppression effectiveness, and conditional escape probability. Overall, IA reduced mean study area BP by 78% as compared to a no-suppression scenario, but the primary finding was marked spatial heterogeneity. IA was most effective in recently burned areas (86% reduction), whereas mature, contiguous fuels moderated its influence (50%). Suppression was least effective in the designated wildfire exclusion zone, suggesting supplementary management approaches may be appropriate. While the framework includes assumptions about IA containment, results offer new insight into emergent risk patterns and how management strategies alter them. Managers can adopt these methods to anticipate, quantify, and compare fine-scale policy outcomes.
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.
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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.005 |
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 it