Bibliographic record
Abstract
Abstract Along the coastal areas of northwest Greenland, sea ice is crucial to people's livelihoods. People in the region have long depended on hunting marine mammals such as seals; walrus; narwhal, beluga, fin, and minke whales; and polar bears, as well as fishing for fjord cod, Greenland halibut, salmon, and Arctic char. Terrestrial animals such as reindeer and Arctic foxes have also been of some importance, as have musk ox in some areas. However, the effects of a changing climate on the marine environment are stark, immediate, and tangible. Ice is melting, and coastal waters are warming. Sea ice, glaciers, coastlines, and seas have become sites and objects for new forms of environmental governance shaped by ideas of unique and fragile ecosystems under threat at a moment of planetary crisis. Conservation organizations frame the Arctic as a zone of climate change crisis and have launched campaigns—underpinned by narratives of ruination—to protect what are termed last areas of ice. However, Inuit organizations are also working to ensure that environmental governance and conservation policymaking do not exclude local communities in the region and are campaigning for protected marine areas in which wildlife management systems and community‐based monitoring take note of indigenous rights and incorporate indigenous knowledge. This article is categorized under: Science of Water > Water and Environmental Change Human Water > Rights to Water Human Water > Water Governance Water and Life > Conservation, Management, and Awareness
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 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".