Sensor Control Effectiveness and Display Design in an Imaging System for Airborne Search and Rescue
Bibliographic record
Abstract
Traditionally, search and rescue (SAR) technicians have conducted search by directly viewing the terrain below the aircraft. Defence R&D Canada is developing a multi-sensor imaging system for SAR that would replace direct inspection under low visibility conditions. The operators may orient the sensors in any direction, which combined with a narrow field-of-view may induce disorientation. In addition to detecting and discriminating objects within the sensor's field-of-view, an operator is responsible for moving the sensor in such a way as to ensure that this detection and discrimination may be carried out with similar effectiveness across the entire area that needs to be searched. The present study reports the development of a metric of coverage effectiveness, and its application to two simulation experiments. The results demonstrate a trade-off between the availability of a moving-map display necessary to preserve operator orientation, and the effectiveness of an operator's sensor coverage over a specific region. That is, sensor control effectiveness was compromised by the addition of a moving-map to the display, likely due to operators' inability to simultaneously inspect the map and move the sensor appropriately. The coverage effectiveness metric reported here would be a useful tool in the development and evaluation of “smart” automation of this sensor sweep function.
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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 | 0.002 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".