Effects of Automated Scanning in a Search and Rescue Detection Task
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 this direct “out-the-window” inspection under low visibility conditions. The system could be designed to automate the sweep of the sensor across the terrain in order to minimize operator workload and to ensure that the sensor covers all of the area to be searched. In a previous prototype, the operator controlled the sweep of the sensor through the use of a joystick, adjusting the speed and direction of the sensor in real time. That interface permitted the operator to both cover the terrain and maximize the detection potential targets by moving the sensor more slowly over portions of the display that was more complex or contained cues to the potential presence of a target. The present study was designed to determine whether automation of the sweep function would compromise detection performance by preventing the operator from adjusting the motion of the sensor to make use of the visual information contained in the scene. The results demonstrate that detection is indeed superior when the sensor sweep is controlled by the operator, and that this effect is modulated by the detectability (contrast) of the target. Additionally, it was observed that the terrain could be more effectively covered under operator control, such that the operator was able to adjust the motion of the sensor to match changes in the visibility of the terrain.
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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 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".