Persistent and emerging threats to Arctic biodiversity and ways to overcome them: a horizon scan
Bibliographic record
Abstract
Arctic biodiversity is under threat from both climate-induced environmental change and anthropogenic activity. However, the rapid rate of change and the challenging conditions for studying Arctic environments mean that many research questions must be answered before we can strategically allocate resources for management. Addressing threats to biodiversity in the Arctic is further complicated by the region's complex geopolitics, as eight countries claim jurisdiction over the area, with multiple local considerations such as Indigenous sovereignty and resource rights. Here, we identify research priorities to serve as a starting point for addressing the most pressing threats to Arctic biodiversity. We began by collecting pressing research questions about Arctic biodiversity, thematizing them as either threats or actions, and then categorizing them further into 18 groups. Then, drawing on cross-disciplinary and global expertise of professionals in Arctic science, management, and policy, we considered the barriers to answering these questions and proposed potential solutions that could be implemented if barriers were overcome. Overall, our horizon scan provides an expert assessment of threats (e.g., species’ responses to climate change) and actions (e.g., a lack of fundamental information regarding Arctic biodiversity) needing attention and is intended to guide future conservation action within the Arctic.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".