Seabird foraging behaviour indicates prey type
Bibliographic record
Abstract
To investigate how a generalist marine predator modifies its foraging behaviour by prey type, we attached time-depth-temperature recorders to chick-rearing thick-billed murres (n = 204) at Coats Island, Nunavut, Canada from 1999 to 2007. Predators varied their behavior along 3 major 'axes': foraging effort, prey depth and prey lifestyle (benthic/pelagic). Dive behaviours for different prey -fish doctor, squid, sandlance, amphipods, snakeblenny, daubed shanny, sandlance and Arctic shanny -were discriminated from one another in a discriminant analysis of dive variables and these prey were therefore considered 'specialist' prey items. Specifically, amphipods were captured during V-shaped dives near the colony with a slow bird descent rate, squid were captured during deep V-shaped dives in cold water and fish doctor were captured during a long series of U-shaped dives in relatively warm water far from the colony. Arctic shanny and snakeblenny tended to be taken at moderate distances from the colony, with snakeblenny taken at deeper depths. Daubed shanny captures showed a bimodal distribution, with some taken at shallow depths far from the colony and others at deep depths close to the colony. Dive behaviours for Arctic cod, capelin and sculpin overlapped both with each other and the behaviours for specialist prey items and, therefore, were classified as 'generalist' prey items. In general, V-shaped dives preceded deliveries of pelagic prey items and U-shaped dives preceded deliveries of benthic prey items. Our results strongly suggest that generalist marine predators use stereotypic behaviour to forage for prey items, based on previous knowledge about what locations/strategies maximized intake for a given prey type.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.002 | 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".