Tab.1: Total estimated muskox numbers and quotas in management zones of the Northwest Territories and Nunavut
Bibliographic record
Abstract
In view of the drastic growth in the Canadian Inuit population, the rising costs of living, the missing job and income alternatives and the high unemployment rate in the arctic, efforts are being made to make use of the muskox populations in order to provide additional sources of food and/or revenue. The present paper attempts to review the course of muskox utilization in the Canadian Arctic and to tentatively assess its present as weIl as its future economic importance. Starting with the pre-European status of muskoxen in Canada, the drastic reduction in numbers resulting from the combined efforts of hide traders, whalers and expedition parties in the 19th and early 20th centuries, the impact of the legal protection and the recovery since 1917 are being described. Establishing muskox farms with semi-domesticated herds failed in Canada in the 1970's. Since 1969, though, increasing numbers of animals have been allotted to many Inuit communities, and despite the fact that most of the animals were primarily used for subsistence purposes, some communities could reserve part of their quotas for trophy (sport) hunters. While controlled sustainable subsistence and trophy hunts may eventually be carried out over the whole muskox range, including recently colonized northern Quebec, commercial harvesting for meat, hides and wool, introduced in 1981, will at least for some time be restricted to Banks and Victoria islands which at present show 78 % of the Canadian muskox population and 94 % of the overall quota.
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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 | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.020 | 0.003 |
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".