Unsealing behaviour: Variation in harbour seal (Phoca vitulina) responses to anthropogenic sound in relation to individual health
Bibliographic record
Abstract
Anthropogenic underwater noise can affect animal behaviour, which in turn is influenced by other intrinsic and extrinsic factors. In this case study, we explored links between behaviour, underwater noise and health on 18 free-ranging harbour seals (Phoca vitulina) in the Elbe estuary and Wadden Sea. Individuals were captured and blood samples were taken to assess the health status through leukograms and molecular biomarkers indicative of stress, sound exposure and immunological status. Seals were fitted with long-term sound and movement tags (DTAGs), recording high-resolution three-dimensional diving behaviour and received sound levels simultaneously. Four behavioural states were identified from the seals' dive data (bottom phase duration, prey capture attempts, bottom phase stroke frequency, post-dive duration, descent velocity) using a Hidden Markov Model, and state transitions were linked to received 2 kHz decidecade levels as vessel noise proxy. State transition probabilities varied, but with increasing noise, bottom resting probability decreased, and transit behaviour likelihood increased. Seals remaining in the Elbe were exposed to vessels regularly and showed higher tolerance to underwater noise exposure than seals exposed to fewer vessels. Immunological health parameters (here white blood cells count) also affected the onset of noise-induced state transitions, highlighting the importance of considering health status in behavioural response studies.
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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 | 0.000 |
| 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 | 0.000 |
| 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".