Economic Vulnerability of Northern Canadian Mining to Winter Road Degradation Due to Climate Change
Bibliographic record
Abstract
The majority of mining activity in Canada’s Northwest Territories (NWT) is inaccessible by permanent ground transportation infrastructure. Mines depend heavily on the seasonal Tibbitt to Contwoyto Winter Road (TCWR) for freight transportation – every year over 200,000 tonnes of supplies are hauled to the mines during a 60 day period. However, with prominent temperature warming trends in the Arctic, the TCWR operating season durations are becoming progressively shorter. As seasons decrease, less freight can be transported by truck and must shift to aviation - a more costly alternative. This research analyses the financial risk involved in the continued reliance on winter road infrastructure for mining activity. An economic model was developed to capture typical freight movements associated with the operation of Diavik, Ekati, and Snap Lake diamond mines. This model reflects the effects of climate change through the variation of two key factors impacting the traffic volume capacity of winter roads: season duration and ice capacity (maximum allowable truckload weight). The financial implications of hauling a fixed amount of freight under various scenarios of TCWR season durations and ice capacity are evaluated. It is assumed that any cargo that cannot be moved by ground transportation due to shorter seasons is flown to the mine sites. The findings of this analysis highlight that based on recent climate trends in the NWT, a typical TCWR season could become 10 days shorter by 2020, resulting in an operating cost increase of more than $40 million CAD for the Canadian diamond mines.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".