Multi-cusped postcanine teeth are associated with zooplankton feeding in phocid seals
Bibliographic record
Abstract
Tooth morphologies often reflect diet in animals. Among marine mammals, a well-known example is the krill-feeding crabeater seal Lobodon carcinophaga, in which complex, comb-like postcanine teeth function as a sieve by retaining krill inside the mouth while expelling water. However, information on tooth morphology and function is scarce for other seal species. A recent bio-logging study found that Baikal seals Pusa sibirica feed on tiny pelagic amphipods at remarkably high rates with highly multi-cusped postcanine teeth, highlighting the need for comparative analyses on tooth morphologies and diets in phocid seals. Here, we quantified postcanine tooth morphology for 13 seal species based on museum skull specimens, with a particular focus on Baikal seals and their related species (genera Pusa and Phoca). Pusa species, including Baikal seals, had more specialized multi-cusped postcanine teeth than Phoca species, reflecting higher zooplankton proportions in their diets. Postcanine teeth of Baikal seals exhibited the highest degree of specialization among Pusa, even when the effect of age-related wear was controlled for. This result agrees with the highest zooplankton preference in this species. Further, we found a strong positive correlation between the degree of specialization in postcanine teeth and zooplankton reliance across phocid seal species. Our findings indicate that the functional role of multi-cusped postcanine teeth as a sieve is not limited to crabeater seals but prevails in many phocid seals feeding on zooplankton.
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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 | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| 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".