Time-Dependent Queueing Approach to Helicopter Allocation for Forest Fire Initial-Attack
Bibliographic record
Abstract
Helicopters are used extensively to transport initial-attack crews to forest fires in the province of Ontario. Each day fire managers must decide how to allocate the available helicopters to initial-at.tack bases. The helitack transport system at each base can be viewed as a multi-channel queue with customers (fires) and servers (helicopters). The authors describe a time-dependent queueing model of the helitack system and use numerical methods to estimate some of its operating characteristics. A dynamic programming model is then used to specify an optimal allocation of the available helicopters to helitack bases. RESUME Des hfelicoptferes sont employer souvent pour transporter les combattants d'attaque initiaie aux incendies forestieres dans la province de l'Ontario. Chaque jour Ies gerants d'operations doivent decider comment attribuer les helicoptferes disponibles aux bases. On peut envisage le systeme de transportation comme un systfeme d'attente avec une ou plusiers chaines (h^licoptferes) et clients (incendies). Les auteurs decrivent un module math^matique du systfeme de transportation par hfelicopteres et ils utilisent les techniques numeriques pour estimer quelques de ses caracteristiques d'operation. Un module de programmation dynamique est utiliser pour specifier une attribution optimal des hfelicoptferes aux bases. 1
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".