White paper on management and utilization of large whales in Greenland. [Presented at the 67th Annual Meeting of the International Whaling Commission].
Bibliographic record
Abstract
Kalaallit Nunaat/Greenland is a Self-Governing part of the Kingdom of Denmark with full legislative and executive responsibility in many fields including the management of natural living resources. Foreign policy (including international organisations) is the responsibility of the Danish Government in consultation with Greenland. Greenlanders have maintained a traditional lifestyle connected to the sea dependent on marine resources, including subsistence hunting. Greenland (2018) has a population of app. 55,900 people living in 17 towns and 81 settlements (2018, West Greenland: 52,635 and East Greenland: 3,242). Inuit comprise about 90 % of the population. Within the IWC context, Greenland’s hunt of large whales falls in the category of Aboriginal Subsistence Whaling (ASW) together with the Chukotka hunt of gray and bowhead whales, the Bequia hunt of humpback whales and the Alaskan hunt of bowhead and gray whales. For aboriginal subsistence whaling the IWC has the following objectives: - ensure risks of extinction not seriously increased (highest priority); - enable harvests in perpetuity appropriate to cultural and nutritional requirements; - maintain stocks at highest net recruitment level and if below that ensure they move towards it. The Greenland hunt for large whales respects those objectives.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".