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Record W2184800464 · doi:10.14430/arctic977

A Study of the Meteorological Conditions Associated with Anomalously Early and Late Openings of a Northwest Territories Winter Road

2010· article· en· W2184800464 on OpenAlexafffundvenueabout
K. Emma Knowland, John R. Gyakum, Charles A. Lin

Bibliographic record

VenueARCTIC · 2010
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicClimate change and permafrost
Canadian institutionsMcGill University
FundersCanadian Foundation for Climate and Atmospheric SciencesNatural Sciences and Engineering Research Council of CanadaGovernment of CanadaU.S. Department of Transportation
KeywordsTeleconnectionArcticClimatologyPhysical geographyGeologyEnvironmental scienceOceanographyGeographyEl Niño Southern Oscillation

Abstract

fetched live from OpenAlex

In the Canadian Arctic, winter roads are engineered across the frozen land, rivers, and lakes. The strength and longevity of these roads depend on particular weather conditions. Our research focuses on the winter road between Tulita and Norman Wells, Northwest Territories, which has been maintained officially by the territorial government since 1982. Statistical analysis of the opening dates for the winter road showed five seasons with extremely early dates and five with extremely late dates. The extremely early-opening seasons are distinguished by anomalously high sea-level pressures, anomalously cold tropospheric air, and northwesterly surface winds during the November prior to the road opening. The extremely late-opening seasons are characterized by an anomalously strong Aleutian low in the preceding November. The extremely late-opening years are correlated with strong El Niño seasons, whereas the extremely early-opening years are not systematically associated with teleconnection patterns. Our analysis of meteorological conditions near Norman Wells, associated with the extreme opening dates for this winter road, may provide planners with more precise information germane to this road construction.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.619
Threshold uncertainty score0.936

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.021
GPT teacher head0.230
Teacher spread0.209 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations18
Published2010
Admission routes4
Has abstractyes

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