Adaptive Engineering of an Embedded System, Engineered for use by Search and Rescue Canines
Bibliographic record
Abstract
In Urban Search and Rescue (US&R) operations, canine teams are deployed to find live patients, and save lives. US&R may benefit from increased levels of situational awareness, through information made available through the use of embedded systems attached to the dogs. One of these is the Canine Pose Estimation (CPE) system. There are many challenges faced with such embedded systems including the engineering of such devices for use in disaster environments. Durability and wireless connectivity in areas with materials that inhibit wireless communications, the safety of the dog wearing the devices, and form factor must be accommodated. All of these factors must be weighed without compromising the accuracy of the application and the timely delivery of its data. This paper discusses the adaptive engineering process and how each of the unique challenges of emergency response embedded systems can be defined and overcome through effective design methods.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".