Bibliographic record
Abstract
CCP6.1 The Global Importance of Climate Change in Polar Regions�������������������������������������������������������������������������������� 2323 CCP6.2 Observed Impacts and Future Risks �������������������������� 2325 CCP6.2.1 Marine and Coastal Ecosystems ����������������������������� 2325 CCP6.2.2 Terrestrial and Freshwater Ecosystems ��������������� 2330 CCP6.2.3 Food, Fibre and Other Ecosystem Products ������ 2331 Frequently Asked Questions FAQ CCP6.1 | How do changes in ecosystems and human systems in the polar regions impact everyone around the globe?How will changes in polar fisheries impact food security and nutrition around the world?����������� 2333 CCP6.2.4 Economic Activities ��������������������������������������������������������� 2335 Frequently Asked Questions FAQ CCP6.2 | Is sea ice reduction in the polar regions driving an increase in shipping traffic?������������������������������ 2336 CCP6.2.5 Arctic Settlements and Communities ������������������ 2336 Box CCP6.1 | Climate Change and the Emergence of Future Arctic Maritime Trade Routes ������������������������������������ 2338 Frequently Asked Questions FAQ CCP6.3 | How have arctic communities adapted to environmental change in the past and will these experiences help them respond now and in the future?������������������������������������������������������������������������������������������������������������ 2339 CCP6.2.6 Human Health and Wellness in the Arctic �������� 2340 Box CCP6.2 | Arctic Indigenous Self-determination in Climate Change Assessment and Decision Making �� 2342 CCP6.3 Key Risks and Adaptation ��������������������������������������������������� 2344
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.447 | 0.233 |
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".