Bowhead whales and whaling in the central and eastern Canadian Arctic, 1970-2021
Bibliographic record
Abstract
The history of bowhead whaling and hunt management in the eastern and central Canadian Arctic is reviewed. Subsistence hunting of bowhead whales by Inuit resumed in the 1990s under co-management arrangements that were part of land-claims settlement agreements. Removals by whaling in both Canada and Greenland have been accounted for in IWC Scientific Committee assessments of the Eastern Canada–West Greenland (EC-WG) stock, but Canada, having withdrawn from IWC membership in 1982, has no legal obligation to consider IWC management advice. From 1994-2021 the total reported catch of bowheads in the central and eastern Canadian Arctic was 39 (not including struck-and-lost whales or whales that died from incidental entanglement in fishing gear). Sixteen different communities, most of which had a long history of bowhead whaling prior to the arrival of commercial whalers, took at least one bowhead over that 27-year period. More than half of the recent catches have been by the communities of Igloolik, Sanijarak, Naujaat and Coral Harbour, all in the Foxe Basin–Repulse Bay–northern Hudson Bay region where at least occasional hunting of bowheads by local people had persisted until well into the 1970s. Greenland’s reported landed catches totaled from 2009-2015, with no successful hunts reported since 2015. Well over a third of the whales landed by both countries combined have been mature females, the most valuable class in terms of potential for population increase. Several factors in addition to hunting and entanglement in fishing gear are likely affecting EC-WG bowheads, including increased exposure to killer whale predation (linked to the massive reduction in sea ice) and other changes in ecological conditions driven primarily by climate change (e.g. more industrial activity, more vessel traffic, more noise). Recent analyses suggest the EC-WG stock of bowheads has grown considerably since the end of commercial whaling, with best estimates of current abundance in the range of 6000-7000 individuals. Even though the population appears capable of sustaining present levels of removal and disturbance, it is important for monitoring efforts to continue in both Canada and Greenland, with regular Indigenous participation. Local observations and traditional knowledge can be valuable sources of information on animal health, behaviour and phenology.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.010 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".