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Record W2334005235 · doi:10.1061/9780784412473.073

Geothermal Aspects for Designing a Water Ballasted Bottom Founded Gas Plant for the Mackenzie River Delta

2012· article· en· W2334005235 on OpenAlexaff
Entzu Hsieh, Alexandre Tchekhovski, Ben Seligman, Alan Carter

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEnvironmental Science
TopicMethane Hydrates and Related Phenomena
Canadian institutionsShell (Canada)
Fundersnot available
KeywordsBARGEBallastPermafrostGeologyExcavationDeltaEnvironmental scienceGeotechnical engineeringMarine engineeringPetroleum engineeringHydrology (agriculture)EngineeringOceanography

Abstract

fetched live from OpenAlex

The gas plant for the proposed Niglintgak Anchor Field is expected to be assembled on a barge, towed to the Mackenzie River Delta and ballasted down onto the bottom of an excavation advanced into the side of a river channel. In order to place the barge onto the excavation, ballast tanks inside the barge will be filled with water pumped from the river. The gap between the barge and side slopes of the excavation will then be backfilled with excavated materials. The ballast water inside the unheated barge may convey thermal impact to the existing permafrost below the barge. During 2006 to 2008, field tests were initiated to investigate the water freezing progression and growth of hydrostatic pressure in the test tank as well as the dynamics of permafrost temperature under the tank. Based on results of the field tests, 3-dimensional geothermal analyses for the gas plant barge were carried out to predict the development and seasonal melt of ice within the ballast tanks and assess permafrost conditions under the barge over the operational life of the structure. Various scenarios of the ballast water freezing were considered.

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)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.979
Threshold uncertainty score0.041

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.023
GPT teacher head0.228
Teacher spread0.205 · 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 designSimulation or modeling
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
Published2012
Admission routes1
Has abstractyes

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