Best practice recommendations for the use of external telemetry devices on pinnipeds
Bibliographic record
Abstract
Abstract Pinnipeds spend large portions of their lives at sea, submerged, or hauled-out on land, often on remote off-shore islands. This fundamentally limits access by researchers to critical parts of pinniped life history and has spurred the development and implementation of a variety of externally attached telemetry devices (ETDs) to collect information about movement patterns, physiology and ecology of marine animals when they cannot be directly observed. ETDs are less invasive and easier to apply than implanted internal devices, making them more widely used. However, ETDs have limited retention times and their use may result in negative short- and long-term consequences including capture myopathy, impacts to energetics, behavior, and entanglement risk. We identify 15 best practice recommendations for the use of ETDs with pinnipeds that address experimental justification, animal capture, tag design, tag attachment, effects assessments, preparation, and reporting. Continued improvement of best practices is critical within the framework of the Three Rs (Reduction, Refinement, Replacement); these best practice recommendations provide current guidance to mitigate known potential negative outcomes for individuals and local populations. These recommendations were developed specifically for pinnipeds; however, they may also be applicable to studies of other marine taxa. We conclude with four desired future directions for the use of ETDs in technology development, validation studies, experimental designs and data sharing.
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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 | 0.220 | 0.346 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.007 | 0.004 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.013 | 0.008 |
| Research integrity | 0.012 | 0.009 |
| Insufficient payload (model declined to judge) | 0.008 | 0.006 |
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".