Productivity of Ontario initial-attack fire crews: results of an expert-judgement elicitation study
Bibliographic record
Abstract
A structured expert-judgement elicitation technique was used to develop probability distributions for fireline production rates for Ontario's three- and four-person initial-attack crews for seven common fuel types and two distinct levels of fire intensity (i.e., low, 500 kW/m; moderate, 1500 kW/m). A total of 141 crew leaders provided 900 estimates of the minimum, maximum, and most likely (mode) time to construct 610 m (2000 ft) of fireline. This information was used to estimate parameters for beta probability distributions for each individual and scenario. Analysis of variance (ANOVA) of the beta-distribution parameters (α and β) and the three time estimates indicated that fuel type, intensity, crew size, and crew-leader experience all have a statistically significant (p < 0.05) influence on estimated crew productivity. The 28 scenario-specific and 7 aggregated distributions and expected values can be used in many operational fire-management activities (e.g., presuppression planning, initial-attack dispatching, initial-fire assessments) and incorporated into initial-attack containment models. These results also provide baseline data on crew productivity that can be used in larger strategic analyses to gauge the benefits of new fire-suppression equipment and techniques for the entire fire-management program.
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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 | 0.015 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".