Development and Flight Testing of a Passive Collection Uncrewed Aircraft System for Dolphin Health Assessment
Bibliographic record
Abstract
Potential anthropogenic stressors on marine fauna continue to increase, yet how marine mammals fare under these conditions remains largely unmeasured. For example, noise sources may trigger a stress response in whales and dolphins, which typically causes health and cognitive deficits in other mammals. Yet, we do not have data of sufficient quality to demonstrated how anthropogenic impacts on these highly protected and unique species. The objective of this project is to collect health data from dolphin blowhole mucus as animals surface to breathe using a custom uncrewed aircraft systems (UAS) developed for cetacean applications. While COTS rotary wing UAS have been used to collect samples from large whales, smaller cetaceans such as dolphins present a greater challenge for sample collection in the wild. Data from observations indicate that the use of standard commercial drones will result in inaccurate data as the system noise will likely increase stress hormone levels and chase subjects away. Furthermore, rotary wing UAS are likely to blow viable samples away through downwash from the spinning blades. However, silent fixed-wing capable UAS will enable accurate estimation of stress levels, pathogen load, and microbiological contaminants through the successful collection of blow samples, since dolphins should be unable hear and see the drone based on observations mapping dolphin field of vision as well as their sensitivity to different drone sounds. The paper summarizes the design, development, and successful demonstration of the PHASM (Passive Health Assessment of Sea Mammals) UAS for collection of blowhole mucus samples from dolphins.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".