The Gray Seal: 80 Years of Insight into Intrinsic and Extrinsic Drivers of Phocid Behavior
Bibliographic record
Abstract
The gray seal is a data-rich species with behavioral studies dating back to the 1940s. The reasons for the wealth of knowledge are partly fortuitous; pioneering naturalists ventured forth to remote island colonies around the UK and Canada to observe the ‘hook-nosed sea-pig’ during their annual breeding seasons. These early qualitative treatises on gray seal behavior ignited further, more quantitative, research interest, which has continued to expand to this day. Several gray seal traits enhance its suitability as a study system for understanding drivers of behavior, such as their ease of observation and site fidelity during breeding, and unique pelage patterns enabling the collection of long-term data on known individuals. Gray seals also inhabit a remarkable variety of habitats, both on land and at sea, facilitating comparative studies of environmental drivers of behavior. These traits have enabled pioneering behavioral research across a wide range of life-history stages. This chapter aims to capture the diversity of behavioral knowledge derived from gray seals, revealing how they interact with one another and their environment. We also highlight new research areas likely to present research opportunities and challenges in the near future, especially in the context of rapidly changing terrestrial and marine environments.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".