Use of tusks by narwhals, Monodon monoceros, in foraging, exploratory, and play behavior
Bibliographic record
Abstract
Despite the universal fascination with the tusk of the narwhal, the function of this long, spiraled tooth is still debated, primarily because few people have observed how narwhals ( Monodon monoceros ) use their tusks in the wild. Using an unmanned aerial vehicle (UAV), we recorded previously unreported interactions between multiple narwhals, Arctic char ( Salvelinus alpinus ) and glaucous gulls ( Larus hyperboreus ) in Canada’s High Arctic. Narwhals were recorded chasing char and using their tusks to hit, manipulate and influence the behavior of fish. Differences in tusk use likely reflected differences in behavioral intent with some actions associated with prey capture and others with exploration and likely play. Kleptoparasitic behavior by gulls when narwhals pursued char near the surface substantially reduced prey capture for narwhals. Associative and interactive behaviors among narwhals were linked to the ecological context including fish density and gull behavior. Some interactions appeared competitive in nature while others may have been communicative and affiliative. This study revealed that narwhals can use their tusks to investigate and manipulate objects, including prey, and deliver sufficient force with their tusks to stun and possibly kill fish. The speed and agility of char combined with kleptoparasitic behavior of gulls indicate that char may be a challenging species to predate while aspects of the narwhals’ actions may include social learning and exploration of a novel prey species, and are the first reported evidence of likely play, specifically exploratory-object play, in narwhals.
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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 | 0.000 |
| 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".