Best practice guidelines for cetacean tagging
Bibliographic record
Abstract
Animal-borne electronic instruments (tags) are valuable tools for collecting information on cetacean physiology, behaviour and ecology, and forenhancing conservation and management policies for cetacean populations. Tags allow researchers to track the movement patterns, habitat use andother aspects of the behaviour of animals that are otherwise difficult to observe. They can even be used to monitor the physiology of a taggedanimal within its changing environment. Such tags are ideal for identifying and predicting responses to anthropogenic threats, thus facilitating thedevelopment of robust mitigation measures. With the increasing need for data best provided by tagging and the increasing availability of tags, suchresearch is becoming more common. Tagging can, however, pose risks to the health and welfare of cetaceans and to personnel involved in taggingoperations. Here we provide ‘best practice’ recommendations for cetacean tag design, deployment and follow-up assessment of tagged individuals,compiled by biologists and veterinarians with significant experience in cetacean tagging. This paper is intended to serve as a resource to assist tagusers, veterinarians, ethics committees and regulatory agency staff in the implementation of high standards of practice, and to promote the trainingof specialists in this area. Standardised terminology for describing tag design and illustrations of tag types and attachment sites are provided, alongwith protocols for tag testing and deployment (both remote and through capture-release), including training of operators. The recommendationsemphasise the importance of ensuring that tagging is ethically and scientifically justified for a particular project and that tagging only be used toaddress bona fide research or conservation questions that are best addressed with tagging, as supported by an exploration of alternative methods.Recommendations are provided for minimising effects on individual animals (e.g. through careful selection of the individual, tag design and implantsterilisation) and for improving knowledge of tagging effects on cetaceans through increased post-tagging monitoring.
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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 | 0.005 | 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".