RescueCASTR: Exploring Photos and Live Streaming to Support Contextual Awareness in the Wilderness Search and Rescue Command Post
Bibliographic record
Abstract
Wilderness search and rescue (WSAR) is a command-and-control activity where a Command team manages field teams scattered across a large area looking for a lost person. The challenge is that it can be difficult for Command to maintain awareness of field teams and the conditions of the field. We designed RescueCASTR, an interface that explores the idea of deploying field teams with wearable cameras that stream live video or sequential photos periodically to Command that aid contextual awareness. We ran a remote user study with WSAR managers to understand the opportunities and challenges of such a system. We found that the awareness provided by the footage could give additional confidence and comfort to Command, while reducing the need for explicit communications. However, it could also impact workers' traditional roles and shift the burden of responsibility toward Command. We conclude that, while wearable-camera footage could be beneficial to Command, they need to have the tools and means to narrow their focus within the abundance of information provided. Furthermore, camera streams should not be thought of as a replacement for more direct communications, but rather as another tool available to help Command supplement their understanding of events in the field and help them narrow their focus.
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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 | 0.000 | 0.002 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".