Effects of external tags on maternal postpartum, offspring body mass and breeding frequency in gray seals <i>Halichoerus grypus</i>
Bibliographic record
Abstract
Abstract Few studies have examined the impacts of externally fitted data‐loggers and telemetry tags on pinnipeds. We tested for instrument effects on body mass of lactating female gray seals and their offspring and probability of pupping in the next breeding season. Known‐age adult females (n = 216) were fitted with instruments in winter, spring, and fall from 1992 to 2018 at Sable Island, Nova Scotia. Of those tagged in spring and fall, 61 of 135 returning females and 59 of their offspring were weighed within 5 days postpartum and 79 pups were weighed at weaning. Instrumented females were assigned to treatments based on tag frontal area sums, tag mass, deployment duration, and acoustic tag presence compared to control females without instruments using linear mixed‐effects models. None of the treatment effects were included in the preferred models predicting birth mass of offspring or probability of breeding in the following year. The small negative effect (−3% to −7%) on postpartum maternal mass and pup weaning mass (−4.7%) for females instrumented in fall may be an artifact as longer spring deployments showed no effect. Overall, we found that the instruments deployed had no detectable negative effects on the maternal and offspring variables measured.
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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 | 0.001 | 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) | 0.001 | 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".