Remote Communication in Wilderness Search and Rescue
Bibliographic record
Abstract
Wilderness search and rescue (WSAR) requires careful communication between workers in different locations. To understand the contexts from which WSAR workers communicate and the challenges they face, we interviewed WSAR workers and observed a mock-WSAR scenario. Our findings illustrate that WSAR workers face challenges in maintaining a shared mental model. This is primarily done through distributed communication using two-way radios and cell phones for text and photo messaging; yet both implicit and explicit communication suffer. WSAR workers send messages for various reasons and share different types of information with varying levels of urgency. This warrants the use of multiple communication modalities and information streams. However, bringing in more modalities introduces the risk of information overload, and thus WSAR workers today still primarily communicate remotely via the radio. Our work demonstrates opportunities for technology to provide implicit communication and awareness remotely, and to help teams maintain a shared mental model even when synchronous realtime communication is sparse. Furthermore, technology should be designed to bring together multiple streams of information and communication while making sure that they are presented in ways that aid WSAR workers rather than overwhelming them.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 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".