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Record W2274457966 · doi:10.13034/jsst.v8i1.48

WARMING CLIMATE DAMAGES NORTHERN ROADS

2015· article· en· W2274457966 on OpenAlexaffvenueabout
Jim Graham, Marolo Alfaro, Hamid Hamid, David Kurz

Bibliographic record

VenueJournal of Student Science and Technology · 2015
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicClimate change and permafrost
Canadian institutionsGolder Associates (Canada)University of Manitoba
Fundersnot available
KeywordsDamagesArcticShoreBayGeographyCold climateHydroelectricityEnvironmental protectionOceanographyArchaeologyGeologyMeteorologyEngineering

Abstract

fetched live from OpenAlex

Canadians are increasingly aware of the needs and opportunities of northern Canada. Communities in the North need additional support in terms of health care, education, employment opportunities, and the high cost of living. Meanwhile, the economic importance of the North is increasing rapidly through development of mineral, petrocarbon, and hydroelectric resources. Reduction of ice cover in the Arctic Ocean is expected to lead to additional shipping in and out of northern ports. New roads are being planned over difficult terrain in Yukon Territory, Northwest Territory, Nunavut, northern Manitoba and northern Quebec. The only rail line in the North - to Churchill on the shores of Hudson’Bay - needs major repairs.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.873
Threshold uncertainty score0.255

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0090.002
Scholarly communication0.0030.001
Open science0.0010.002
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0240.003

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 head0.042
GPT teacher head0.294
Teacher spread0.251 · 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 source (direct Gemma or distilled Codex), 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

Citations0
Published2015
Admission routes3
Has abstractyes

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