Tags below three percent of body mass increase nest abandonment by rhinoceros auklets, but handling impacts decline as breeding progresses
Bibliographic record
Abstract
Biologging has revealed many of the mysteries surrounding seabird behavior far from land. However, tagging seabirds with biologgers may influence the very traits they are designed to observe. Such ‘tag effects’ are often argued to be minimal below a threshold of 3% of body mass. Nonetheless, few studies carefully separate handling from tagging effects, so the effect of tag size is often confounded with the effect of handling. Puffins, including rhinoceros auklets Cerorhinca monocerata, are notoriously difficult to work with due to high nest abandonment rates. To examine tagging and handling effects in rhinoceros auklets, we compared abandonment rates of individuals equipped with a GPS weighing ~2.3% of body mass with abandonment rates of birds handled but not equipped, and of birds not handled at all (controls). We used the egg flotation technique to estimate egg development and predict hatching date, thus allowing treatments to be applied at the appropriate time. Handling more than doubled abandonment rates compared to control birds, and tagging more than doubled abandonment rates compared to birds that were handled but not tagged. Abandonment rates decreased as incubation progressed and were lowest during chick-rearing. We conclude that both handling and tagging of auklets increase abandonment, and that effects are lowest during chick-rearing.
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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 | 0.001 |
| 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".