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Record W2007853637 · doi:10.1115/ipc2006-10616

Thaw Responses in Degrading Permafrost

2006· article· en· W2007853637 on OpenAlexaboutno aff
Jim Oswell, Darren Skibinsky

Bibliographic record

VenueVolume 1: Project Management; Design and Construction; Environmental Issues; GIS/Database Development; Innovative Projects and Emerging Issues; Operations and Maintenance; Pipelining in Northern Environments; Standards and Regulations · 2006
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicClimate change and permafrost
Canadian institutionsnot available
Fundersnot available
KeywordsPermafrostPiezometerGeotechnical engineeringGeologyClearanceEnvironmental scienceGroundwaterAquifer

Abstract

fetched live from OpenAlex

The Norman Wells pipeline has operated for over 20 years, transporting crude oil from Norman Wells, Northwest Territories to Zama, Alberta. The pipeline route traverses 869 km of discontinuous permafrost. The stability of the slopes along the route required that rapid thawing of ice-rich permafrost be avoided, lest the high porewater pressures that develop on thawing would cause instability. To reduce the thawing rate, a layer of wood chips was used as surface insulation. Approximately one-half of the insulated slopes were instrumented with thermistors and piezometers to monitor the thawing and the development of porewater pressures. This paper compares the actual performance of the insulated and non-insulated slopes to the original design predictions. Thaw depth is presented in terms of the square root of time. The likely original design intent of insulated sites was to restore a level of surface insulation that would represent a “cleared but otherwise undisturbed surface condition”. The actual performance of most of these sites was more dramatic than this. Factors that may have contributed to the greater than expected thawing are examined, including site orientation, preclearing, and soil type.

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)0.001
metaresearch head score (Gemma)0.002
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
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 head0.251
Teacher spread0.230 · 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

Citations1
Published2006
Admission routes1
Has abstractyes

Explore more

Same venueVolume 1: Project Management; Design and Construction; Environmental Issues; GIS/Database Development; Innovative Projects and Emerging Issues; Operations and Maintenance; Pipelining in Northern Environments; Standards and Regulations→Same topicClimate change and permafrost→French-language works237,207→