Distribution and abundance of killer whales (<i>Orcinus orca</i>) in Nunavut, Canada—an Inuit knowledge survey
Bibliographic record
Abstract
Traditional ecological knowledge is being increasingly used in wildlife management in northern regions, and Inuit harvesters in Nunavut, Canada, have extensive knowledge about local wildlife species. We collected Inuit knowledge on killer whales (Orcinus orca) through 105 semi-directed interviews in 11 Nunavut communities from 2007 to 2010. Interviewees provided extensive information on killer whale movements, seasonal presence, distribution and abundance in the eastern Canadian Arctic. Observations from different communities were often complementary, and there was consistency in interview comments both within and among regions. Nearly all participants had seen killer whales at least once, and the whales were present every summer (July–September) in all regions, although movements depended on ice conditions. Relative abundance of killer whales varied by region, and they were reported more often in North Baffin communities than in other regions. Killer whales migrated through Hudson Strait and Lancaster Sound following their marine mammal prey. Estimates of local population sizes were variable, with suggested numbers that varied from tens to the low hundreds. Most interviewees in the Foxe Basin, Hudson Bay and north Baffin regions thought that killer whale presence was increasing. In contrast, half the South Baffin interviewees noted declines in past abundance due to the 1977 harvest of 14 whales that became trapped in a saltwater lake. Interviews provided information at a long temporal and wide spatial record. Inuit are reliable observers and continued killer whale research will be most effective if it integrates modern science approaches with the traditional skills, knowledge and experience of Inuit harvesters.
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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 | 0.002 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".