Understanding regional variability in water, energy, and food (WEF) security: an Arctic case-study
Bibliographic record
Abstract
Arctic nations are among the most water, energy, and food (WEF) secure nations of the world. National indices can, however, mask intra-regional disparities that exist within nations. The variable conditions of remote and sparsely populated regions are at particular risk of being obscured through the aggregation of national-level reporting. In this paper, we present the results of a regional (N = 27) assessment of WEF security in six Arctic states. Our assessment found considerable variability that ranged from highly secure to highly insecure regions that differ dramatically from state-level reporting. With few exceptions, Alaska and northern Canada suffer from higher rates of WEF insecurity than the northern regions of Iceland, Norway, Sweden and Finland. Although logistical, demographic, and climatic conditions contribute to these differing conditions, inequitable public investment in WEF services is a major constraint to service provisioning. Whereas regions in the European Arctic enjoy conditions of relative WEF security, they too may be challenged to maintain these conditions and adapt to not only a changing climate but also geo-political uncertainties that could obstruct the delivery of WEF services. Being attentive to dynamic conditions will be a critical and necessary step to achieving sustained WEF security in all Arctic regions.
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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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".