Longitudinal changes and consistency in male physical and behavioural traits have implications for mating success in the grey seal (<i>Halichoerus grypus</i>)
Bibliographic record
Abstract
We examined age-related changes and consistency in physical and behavioural traits of 20 male grey seals ( Halichoerus grypus (Fabricius, 1791)) and implications for a proxy of mating success (number of oestrous females attended) over four successive breeding seasons on Sable Island, Canada. Across the study, young males (10–15 years) gained body mass, while old males (23–31 years) lost body mass. Body length was an important determinant of tenure (time spent at a site among females) and males of all ages exhibited a high level of consistency in duration of tenure (r = 0.40–0.50). In young males, our proxy of success showed a strong relationship with arrival body mass and also exhibited a high level of consistency (r = 0.50). None of the physical traits measured explained variation in success by exhibiting mating tactics that did not involve tenure, which is likely due to the opportunistic nature of those tactics. Whereas young male grey seals exhibited age-dependent improvements in success owing to changes in their physical state, later in life physical traits were less influential and suggest that nonphysical traits may compensate for a deteriorating physical state and its impact on male success.
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 | 0.000 | 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) | 0.001 | 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".