Diving behavior of Magellanic penguins (<i>Spheniscus magellanicus</i>) at Punta Tombo, Argentina
Bibliographic record
Abstract
Geographic and temporal variability in the marine environment affects seabirds' ability to find food. Similarly, an individual's body size or condition may influence their ability to capture prey. We examined the diving behavior of Magellanic penguins (Spheniscus magellanicus) at Punta Tombo, Argentina, as an indicator of variation in foraging ability. We studied how body size affected diving capability and how diving varies among years and within breeding seasons. We also compared diving patterns of Magellanic penguins at Punta Tombo with those of birds in two colonies at the opposite end of the species' breeding range. Larger penguins tended to dive deeper and for longer than smaller birds. Trips were longer during incubation and in the years and colonies with lower reproductive success, which suggests that in those instances birds were working hard to recover body condition and feed chicks. Average dive depths, average dive durations, and percentages of time spent diving were always similar. We found that the only parameter these penguins consistently modified while foraging was the length of their foraging trip, which suggests that penguins at Punta Tombo were diving at maximum rates to find their preferred prey. Increasing trip length, we suggest, is a physiologically conservative solution for increasing the likelihood of encountering prey.
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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".