Determining Freshwater Lake Communities’ Vulnerability to Snowstorms in the Northwest Territories
Bibliographic record
Abstract
As the exposure to extreme snowstorms continues to change in response to a warming climate, this can lead to higher infrastructure damages, financial instability, accessibility restrictions, as well as safety and health effects. However, it is challenging to quantify the impacts associated with the combination of the many biophysical and socio-economic factors for resiliency and adaptation assessments across many disciplines on multiple spatial and temporal scales. This study applies a framework to quantitatively determine the multiple impacts of snowstorms by calculating the livelihood vulnerability index (LVI) for four exposed freshwater lake communities in Canada’s Northwest Territories using three contributing factors (exposure, sensitivity, and adaptive capacity). Results indicate that Déline is the most vulnerable community (0.67), because it has the highest exposure and one of the highest sensitivity ranks, while its ability to adapt to exposure stressors is the lowest among the communities. In contrast, Fort Resolution exhibits the lowest LVI (0.26) and has one of the highest adaptive capacities. This study emphasizes that while these freshwater communities may be exposed to snowstorms, they have different levels of sensitivity and adaptive capacities in place that influences their vulnerability to changes in hazardous snowfall conditions. The information gained from this study can help guide future adaptation, mitigation, and resiliency practices for Arctic sustainability efforts.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".