Inuit Hunt as a Platform for Observing Narwhals ( <i>Monodon monoceros</i> ) in Inglefield Bredning (Kangerlussuaq), Greenland
Bibliographic record
Abstract
Abstract Direct observations of narwhals are scarce but needed for understanding this ecologically and culturally important species. Here, we describe the first boat-based observations of narwhals in their key summering ground in Greenland (Inglefield Bredning), collected during an Inuit hunt and enhanced by drone air support. During 3—8 August 2024, 506 narwhal observations were made from a semi-stationary boat at the head of the fjord, of which 58 were filmed with a drone. Boat observations near the north side of the fjord indicated that narwhals preferentially traveled outward to the west, with a clear link to currents. The presence of narwhals was more likely in the second half of a day but was highly intermittent, with waiting times for observers reaching as much as 15–28 h between sightings, highlighting the patience needed to observe and catch a narwhal. We also recorded a motionless sleep-like behavior at the surface, known as pugginnartoq (Greenlandic) for narwhals. Aerial drone support was useful for revealing unseen-from-sea-surface features and behaviors, which we describe as potentially interesting for future investigation. For example, drone imagery revealed that 71±7% of narwhals had tusks, with a mean tusk-to-body-length ratio of 0.23±0.01. Overall, this report shows that hunting expeditions integrated with scientific methods provide important insights and inspire further work.
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".