Filtration area scaling and evolution in mysticetes: trophic niche partitioning and the curious cases of sei and pygmy right whales
Bibliographic record
Abstract
We analysed the functional morphology and hydrodynamics of the filtering apparatus in ten species of baleen whales (Mysticeti). Our results demonstrate a clear demarcation in baleen scaling of continuous ram filter feeders (Balaenidae; right and bowhead whales) and intermittent lunge/suction feeders: rorquals (Balaenopteridae) and the grey whale (Eschrichtiidae). In addition to different scaling trajectories, filter area varies widely among taxa. Balaenid baleen has four to five times the area of that of similarly sized rorquals (by body length and mass). Filter areas correlate with morphology; lineages evidently evolved to exploit different types of patchy prey. Feeding performance data from hydrodynamic modelling and tagged whales suggest that drag forces limit balaenids, whereas time required to purge and filter engulfed water appears to limit rorquals. Because scaling of engulfment volume outpaces increases in baleen area, large rorquals must devote greater proportions of dive time to filtration. In contrast, balaenids extend dive duration, but as a trade-off are limited to low engulfment speeds and therefore can only target prey with low escape capabilities. The sei whale, Balaenoptera borealis, has a mid-range filter reflecting its transitional diet and intermediate morphology, embodying generalized characteristics of both continuous ram and intermittent lunge filtration. The pygmy right whale, Caperea marginata, has a balaenid-type filter via 2D analysis, but enhanced 3D modelling shows Caperea's baleen fits better with rorquals. Allometric equations relating body and filter size address phylogenetic questions about filtration in extinct lineages, including future ancestor state reconstruction analyses. Based on baleen and body size (~5 m) and skull morphology, the earliest edentulous mysticetes were probably intermittent rather than continuous filterers, with simple baleen.
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How this classification was reachedexpand
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 | 0.001 | 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".