Considering the fate of electronic tags: interactions with stakeholders and user responsibility when encountering tagged aquatic animals
Bibliographic record
Abstract
Summary The use of electronic tagging (e.g. acoustic, archival and satellite telemetry) to study the behavior and ecology of aquatic animals has increased dramatically over the past decade. As scientists continue to use these tools, it is inevitable that other researchers and the public at‐large will encounter animals carrying such tags with increasing frequency. If the animals appear burdened or injured by the tag (e.g. showing signs of trauma), or if the tag is functionally impaired (e.g. cracked or severely biofouled), these encounters have the potential to generate conflict with various wildlife stakeholders (e.g. tourists/operators, divers, fishers, hunters) that can negatively affect research efforts and undermine conservation work. Yet, these encounters also present an unparalleled opportunity to advance the field of biotelemetry by improving animal welfare, tagging technology and practices, while also gaining the trust and support of wildlife stakeholders. Therefore, as scientists, it is important to consider the fate of our electronic tags. Here we consider tagged animals as encountered by different user groups and discuss the potential steps and recommendations that scientists can take to improve tagging techniques and animal welfare as a result. We also discuss interactions with stakeholders and the manifold benefits if such interactions are taken into account and embraced. We examine the situation where a researcher encounters, and is able and trained to handle a previously tagged animal equipped with a functionally impaired tag and/or the animal is exhibiting signs of burden due to the tag. We generate a decision tree for scientists faced with such a scenario and discuss the best course of action, whereas such a situation was relatively unlikely in the past, but is now a reality in all aquatic animal tagging studies. The framework in which these issues are discussed is novel and failure to address them can significantly impede advances in the development and use of biotelemetry and even one's ability to conduct research. It is our hope that our essay stimulates further discourse, debate, technological improvements and consideration of the fate of electronic tagging.
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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 | 0.031 | 0.059 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.005 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".