Biological parameters in a declining population of narwhals (<i>Monodon monoceros</i>) in Scoresby Sound, Southeast Greenland
Bibliographic record
Abstract
A decreasing trend in narwhal (Monodon monoceros Linnaeus, 1758) abundance has been identified in a small population in Scoresby Sound, Southeast Greenland. We hypothesize that excessive hunting has affected life history and population dynamics of this population. Biological information and samples collected from the Inuit hunt, from satellite-tagged narwhals and from official hunters’ reports, were used to estimate age, growth, and reproduction. During 2007 through 2019, a decreasing proportion of young and increasing proportion of older whales were harvested. Male and female body length and male tusk length increased significantly, while body mass of both sexes showed a nonsignificant increase. The probability of catching a female decreased significantly, while a nonsignificant decline of catching a pregnant female was observed in both biological samples and hunters’ reports. Narwhal swimming speeds correlated with fluke widths indicated that larger whales attain greater speeds. The decline in juveniles and females is probably due to an opportunistic hunting practice targeting the easiest-to-catch whales, where bigger whales are faster and more difficult to catch. The cumulative effect of overharvest with a declining proportion of females, an overrepresentation of large males, and a lack of calves and juveniles has detrimental implications for this small narwhal population.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".