Effect of search window size on search and rescue call-around performance
Bibliographic record
Abstract
The Fixed Wing Search and Rescue (FWSAR) project tasked Defence Research and Development Canada (DRDC) to provide guidance on the primary search window requirements for a new SAR aircraft. At issue was the effect of the size and location of the SAR technician's window on the SAR technician's ability to verbally guide a pilot to fly the aircraft over a search object. An answer was obtained through a two-stage approach. First, data were collected using a simulation of the call-around. In the synthetic environment, six SAR technicians performed a large number of call-arounds where the search window size and observer position in a simulated aircraft were adjusted on a trial-by-trial basis. Then, a live flying trial at CFB Comox involving two SAR technicians was conducted to validate the results obtained from the synthetic environment. Three recommendations emerged. First, the primary search window should provide a field of view of at least 160 . Second, performance is not affected by the visual effect of placing the window ahead of, or behind, the wing. Third, to obtain the full benefit of the field of view afforded by the window, the observer must be provided with an ergonomically sound work station.
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 | 0.001 | 0.016 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".