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Record W2598651160 · doi:10.1061/9780784480434.025

Geomembrane Water Proofing of a 16.3 km Hydroelectric Structural Wood Flume

2017· article· en· W2598651160 on OpenAlexaff
Brian W. Fraser, Mike Neal, Ross Hartsock

Bibliographic record

VenueGeotechnical Frontiers 2017 · 2017
Typearticle
Languageen
FieldEngineering
TopicUnderground infrastructure and sustainability
Canadian institutionsTC Scientific (Canada)
Fundersnot available
KeywordsFlumeGeomembraneHydroelectricityEnvironmental scienceCulvertGeotextileGeotechnical engineeringEngineeringCivil engineering

Abstract

fetched live from OpenAlex

In 2015 the authors undertook to refurbish a 16.3 km (10.1 mile) elevated wood and metal flume that was owned by a private hydroelectric company near Mount Rainier, Washington. The hydroelectric facility was originally built in the early 1900s. Water is diverted from the Puyallup River at an intake diversion and carried by a wood flume structure downstream to the hydroelectric generating plant. The flume is supported by approximately 6,200 beams of which approximately 1,200 are wooden. In many areas of the flume, the wood floors and walls were badly decayed and leaking. The plant had an operating capacity of 26 megawatts (MW) however as a result of the structural decay the flume was experiencing significant water leakage resulting in the plant only operating at 8 MW. The project started with 165,000 m2 of a high strength geotextile that was installed below the geomembrane to help structurally reinforce the deteriorating wood flume walls and flooring. The water proofing of the flume required a total of 140,000 m2 of 2.5 mm and 2.0 mm HDPE liner material to be installed. The project faced many challenges as a result of poor access and tight space constraints. There was no roadway or vehicle access other than three staging areas. All project related materials and equipment needed to be deployed using a small rail car and track built into the top of the flume structure. Crews had to deploy, weld and mechanically attach geomembrane while working in a very confined working space.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.332
Threshold uncertainty score0.886

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
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 head0.005
GPT teacher head0.209
Teacher spread0.204 · 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 designBench or experimental
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
Published2017
Admission routes1
Has abstractyes

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