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Record W565133682

Economic Vulnerability of Northern Canadian Mining to Winter Road Degradation Due to Climate Change

2015· article· en· W565133682 on OpenAlexaboutno aff
Jane Borkovic, Alexandre Nolet, Matthew J. Roorda

Bibliographic record

VenueTransportation Research Board 94th Annual MeetingTransportation Research Board · 2015
Typearticle
Languageen
FieldSocial Sciences
TopicArctic and Russian Policy Studies
Canadian institutionsnot available
Fundersnot available
KeywordsClimate changeEnvironmental scienceTruckArcticGeographyEngineeringOceanographyGeology
DOInot available

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.012
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.287
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0120.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.002
Science and technology studies0.0020.001
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.131
GPT teacher head0.422
Teacher spread0.291 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2015
Admission routes1
Has abstractyes

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