Bibliographic record
Abstract
Fire suppression is (functionally) effective insofar as it reduces area burned. In North American boreal forests, fire regimes and historical records are such that this effect cannot be detected or estimated directly. I present an indirect approach, proceeding from the practice of initial attack (IA), which is intended to limit the proportion of "large" fires. I analysed IA's (operational) effectiveness by a controlled retrospective study of fire-history data for an approximately 86 000 km 2 region of boreal forest in northeastern Alberta, Canada, from 1968 to 1998 (31 years). Over this interval, various improvements to IA practice, including a 1983 change in management strategy, created a natural experiment. I tested the results with multiple logistic regression models of the annual probabilities of a fire becoming larger than 3 and 200 ha. Annual fire counts (N t ) were a surrogate for fire weather and peak daily counts within years (arrival load). Measured by odds ratios, mean IA effectiveness against 3- and 200-ha fires increased in 1983 by factors of 2.02 (95% CI = 1.702.40) and 2.41 (95% CI = 1.693.45), respectively. Prior to 1983, the functional response to N t was consistent with saturation of IA capacity at high arrival loads. From 19831998, effectiveness was independent of N t . I introduce the proportional reduction in area burned (impact) as a measure of functional effectiveness and state conditions under which it can be estimated from the regression models. Over 19831998, if suppressed and actual fires were comparable, relative IA impact ([Formula: see text]) was 0.58 (95% CI = 0.340.74) and area burned was reduced by 457 500 ha. If fires larger than 1 × 10 5 , 1 × 10 4 , or 1 × 10 3 ha are assumed to be unpreventable, [Formula: see text] declines to 0.46, 025, or 0.08, respectively, but there is no evidence this is the case.
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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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".