Drone-based application of whale tags: A “tap-and-go” approach for scientific animal-borne investigations
Bibliographic record
Abstract
Deploying animal-borne suction-based tag devices on whales has been one of the primary tools used by researchers over the past several decades to gather high-resolution scientific information, such as bioacoustics, heart rate, dive depth, and body orientation. However, the process of successfully applying animal-borne tags is logistically challenging and requires substantial operator skill. Current methods apply tags by approaching the whale in a boat and adhering the tag via a long extension pole. In this study, we explore an alternative approach to apply animal-borne suction-based tag devices using First Person View (FPV) racing drones. These drones have been specifically adapted to withstand exposure to seawater, allowing them to operate effectively in marine environments. The drones are equipped with a custom interface, allowing to release the tag when it is applied on the whale's back. In this study, we present the development of the delivery drone as well as tag deployment techniques. The proposed method was demonstrated on sperm whales (Physeter macrocephalus) off Dominica, resulting in fast deployment time (one minute and fifteen seconds on average) and a relatively high deployment success rate (over 55 %). In addition, the presented deployment process offers a less invasive technique for tagging, as boats are not needed for close approaches. These methods also serve as a framework to enable future development of more automated solutions to apply the tag on exact anatomical targets with controlled initial adhesion pressure and without manual operation.
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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 | 0.000 |
| 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 | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".