Climate change resilience in the Canadian Arctic: The need for collaboration in the face of a changing landscape
Bibliographic record
Abstract
Human‐induced changes to global climate have become increasingly difficult to ignore in recent years. As the frequency and severity of extreme weather events increases, the impacts on both natural and human systems are becoming difficult to manage with the current policies. In Canada, one of the most vulnerable regions to climate change is the Arctic, where temperatures are rising at a rate two to three times that of the global average. Warmer seasonal temperatures have led to melting permafrost and increased variability in sea ice conditions, which has contributed to a rise in coastal erosion. The ongoing resilience of Arctic communities will depend heavily on their ability to implement successful long‐term adaptation policies. The development and implementation of any action on climate change adaptation should involve collaboration with local stakeholders in order to reflect the views and experience of those living in the Arctic.
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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 | 0.021 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.059 | 0.017 |
| Scholarly communication | 0.014 | 0.006 |
| Open science | 0.005 | 0.019 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
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".