Foraging rates of ram‐filtering North Atlantic right whales
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Bibliographic record
Abstract
Abstract North Atlantic right whales spend their summer months foraging primarily in American and Canadian Atlantic waters on high‐energy‐density prey. Here, they rapidly accumulate and store energy obtained within a few months to support future migrations and reproduction while fasting. High drag from their ram‐filter foraging strategy places a limit on what prey densities will be energetically efficient to target. Our understanding of the volume of prey‐laden water filtered by right whales during a dive or foraging bout, and what information they use to decide to forage or not, has been limited by the difficulties of measuring when they feed at depth, how fast they swim during continuous ram filtration and how often they might swallow accumulated prey. We used 10 DTAG deployments from right whales in the Bay of Fundy, Canada, to quantify swimming speeds and estimate the volume of prey‐laden water filtered per dive. We used the tag's inertial sensors to evaluate the timing of frequent biomechanical changes that likely indicate the truncation of continuous filtration, and whether the number or timing of these fluking bouts relates to longer feeding dives or other foraging decisions. During foraging dives, right whales descended at 1.4 (±0.2 SD ) m/s and slowed to swim at 1.1 (±0.3) m/s while filtering. We found consistent pauses in the fluking behaviour of foraging right whales, every 56 (±22) s. Whales filtered on average 78 (±30) m 3 of water per fluking bout and on average 673 (±201) m 3 per dive. Right whales filter large volumes of water at low speeds with a high duty cycle, but require sufficiently high prey energy densities to compensate for a high‐drag foraging strategy. Closely related bowhead whales have a larger gape but swim more slowly, filtering greater volumes with lower drag. Our findings highlight that right whales acquire their energy in a relatively short period of intense foraging; even moderate changes in their feeding behaviour or their prey energy density are likely to negatively impact their yearly energy budgets and therefore reduce fitness substantially. A plain language summary is available for this article.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.025 | 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 it