Bowhead whales use two foraging strategies in response to fine-scale differences in zooplankton vertical distribution
Bibliographic record
Abstract
As zooplanktivorous predators, bowhead whales (Balaena mysticetus) must routinely locate patches of prey that are energy-rich enough to meet their metabolic needs. However, little is known about how the quality and quantity of prey might influence their feeding behaviours. We addressed this question using a new approach that included: (1) multi-scale biologging and unmanned aerial system observations of bowhead whales in Cumberland Sound, Nunavut (Canada), and (2) an optical plankton counter (OPC) and net collections to identify and enumerate copepod prey species through the water column. The OPC data revealed two prey layers comprised almost exclusively of lipid-rich calanoid copepods. The deep layer contained fewer, but larger, particles (10% greater overall biomass) than the shallow prey layer. Dive data indicated that the whales conducted long deep Square-shaped dives (80% of dives; averaging depth of 260.4 m) and short shallow Square-shaped dives (16%; averaging depth of 22.5 m) to feed. The whales tended to dive proportionally more to the greater biomass of zooplankton that occurred at depth. Combining behavioural recordings with prey sampling showed a more complex feeding ecology than previously understood, and provides a means to evaluate the energetic balance of individuals under current environmental conditions.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".