Bibliographic record
Abstract
From basic physiology texts to studies of consummate diving animals, the notion that diving capacity increases with body size follows intuitively from the fact that bigger bodies denote larger oxygen stores, but lower metabolic rates. Recent research by Jeremy Goldbogen at Cascadia Research Collective and collaborators in the US and Canada, however, demonstrates that in the most massive breath-hold divers, size matters in a different manner. Large rorquals (blue, fin and humpback whales) forage by lunge feeding, an extraordinary process by which these mammoths of the sea accelerate to high speed, engulf immense volumes of prey-laden water, and purge and filter their big gulps. Such exceptional feeding manoeuvres are not without consequence, however, as this technique requires high drag and incurs great energetic costs for these colossal creatures. Not surprisingly, these whales don't dive for as long as other large divers, including other whales that don't partake in lunge feeding. After generating a bounty of data on the foraging kinematics and energetics of baleen whales, Goldbogen and colleagues were inspired to take a closer look at the scaling of lunge-feeding performance in these titanic filter feeders. Bigger mouths mean bigger gulps for bigger whales, but as the energetic requirements of feeding are also predicted to increase with size, they hypothesized that the cost of a lunge might be disproportionately higher in large rorquals, thereby limiting their dive capacity.The researchers compared diving and lunge-feeding performance among three species of rorquals foraging on krill, ranging in size from the (relatively!) modest humpback whale, to the mid-sized fin whale, and the heftiest animal on Earth, the blue whale. They gathered morphological data (body mass, length, etc.) and set out to derive parameters such as mouth and frontal body area, using geometric models and measurements like skull width and jaw length to estimate gulp size. Employing data from previously deployed acoustic tags and time–depth recorders, Goldbogen and colleagues obtained swimming speed, dive duration and depth, and dive and lunge profiles. Finally, they applied hydro-mechanical and energetic models to estimate the drag required for gulps and the energy exhausted during lunges.The team revealed that not only is drag relatively higher in bigger whales, as a result of a larger mouth area, but also lunge speed increases with body size, further increasing drag in larger whales. Although maximum dive duration and depth were not different between the three species, the largest whales took longer to filter their mouthfuls. This means that humpbacks can lunge more per dive (and per minute of dive) than can fin or blue whales. Higher, sustained drag in larger rorquals requires more energy. When accounting for mass differences, the cost per gulp for blue and fin whales was three- and two-times higher, respectively, than for humpbacks. Despite a host of advantages stemming from the ability to make long dives (access to deeper waters, more time to search for and exploit prey, etc.), these behemoths forfeit enhanced diving capacity in favour of optimized gulps. This trade-off sets large rorquals apart from other true divers, probably allowing them to cash in on patchily distributed prey aggregations by taking gargantuan gulps.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 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 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".