Use of night vision goggles for aerial forest fire detection
Bibliographic record
Abstract
Night-time flight searches using night vision goggles have the potential to improve early aerial detection of forest fires, which could in turn improve suppression effectiveness and reduce costs. Two sets of flight trials explored this potential in an operational context. With a clear line of sight, fires could be seen from many kilometres away (on average 3584 m for controlled point sources and 6678 m for real fires). Observers needed to be nearer to identify a light as a potential source worthy of further investigation. The average discrimination distance, at which a source could be confidently determined to be a fire or other bright light source, was 1193 m (95% CI: 944 to 1442 m). The hit rate was 68% over the course of the controlled experiment, higher than expectations based on the use of small fire sources and novice observers. The hit rate showed improvement over time, likely because of observers becoming familiar with the task and terrain. Night vision goggles enable sensitive detection of small fires, including those that were very difficult to detect during daytime patrols. The results demonstrate that small fires can be detected and reliably discriminated at night using night vision goggles at distances comparable to those recorded for daytime aerial detection patrols.
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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 | 0.002 |
| 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 | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".