Conceptual optimization of remotely piloted amphibious aircraft for wildfire air attack
Bibliographic record
Abstract
In this study, a methodology for the high-level conceptual design, optimization, and evaluation of amphibious remotely piloted and autonomous fixed-wing aircraft to support wildfire air attack strategies is presented. Of particular interest are questions of scale, water source utilization, and optimization of high-level aircraft parameters in a regional context. The Canadian province of British Columbia is used as a case study due to the relevance of wildfire control in that region. The present strategy incorporates a detailed analysis of available water bodies, tanker base locations, and their distance from historical wildfire locations and explores how these regionally specific details impact optimal aircraft design parameters. Results are obtained for optimal lake size as well as the primary design characteristics of the corresponding optimal aircraft. Two filling strategies are evaluated, namely, a “stop-and-go” strategy and a traditional skimming strategy. The results indicate the potential of fleets of optimized aircraft to supply high flow rates while capitalizing on the established benefits of using remotely piloted and autonomous systems. It is hoped this work will encourage future study into improved models and the further development of drone technology for this application, including necessary beyond visual line-of-sight technology and infrastructure.
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.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".