Best practice recommendations for the use of fully implanted telemetry devices in pinnipeds
Bibliographic record
Abstract
Electronic telemetry devices have enabled many novel and important data collection and experimental opportunities for difficult to observe species. Externally attached devices have limited retention and may affect thermoregulation, energetics, social and reproductive behavior, visibility, predation risk and entanglement. Internally placed, surgically implanted devices can mitigate some of these effects and may open additional experimental opportunities. However, improper implementation can significantly affect animals and data. From a review of recent studies using fully implanted tags and studying their effects, we present 15 specific best practice recommendations for the use of such tags in pinnipeds. Recommendations address issues including device size, coating and sterilization, implantation surgery and effect assessment, within the framework of the Three R’s: Reduction, Refinement, Replacement. While developed for pinnipeds, these recommendations could apply to other aquatic mammals and vertebrates and to partially implanted or even external tags.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.071 | 0.180 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.012 | 0.005 |
| Research integrity | 0.015 | 0.007 |
| Insufficient payload (model declined to judge) | 0.013 | 0.010 |
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".