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Record W2133545612 · doi:10.1520/jai102852

Design and Performance of Plastic Drainage Pipes in Environmental Containment Facilities

2011· article· en· W2133545612 on OpenAlexaff
R.W.I. Brachman

Bibliographic record

VenueJournal of ASTM International · 2011
Typearticle
Languageen
FieldEnvironmental Science
TopicLandfill Environmental Impact Studies
Canadian institutionsQueen's University
Fundersnot available
KeywordsContainment (computer programming)DrainageMaterials scienceEnvironmental scienceWaste managementForensic engineeringEngineeringComputer science

Abstract

fetched live from OpenAlex

Abstract Plastic drainage pipes are an essential component of modern engineered barrier systems used in municipal and hazardous solid waste landfills. The design and performance of plastic pipes in these applications is significantly different than conventional municipal sewers and culverts. For example, the plastic pipe may be surrounded by very coarse gravel backfill which results in additional local bending stresses from the irregular loading and support for the pipe. These plastic pipes also experience local stress concentrations from the perforations required to collect fluid. Both of these factors are exacerbated by potentially large overburden pressures acting above the pipe. They must also perform their function while being exposed to chemicals in landfill leachate for long periods of time and at temperatures significantly larger than those for conventional sewers and culverts. The influence of coarse gravel backfill, large perforations, large overburden stresses, chemical exposure and elevated temperatures on the long-term performance of plastic drainage pipes is examined.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0010.000
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 head0.016
GPT teacher head0.199
Teacher spread0.183 · 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 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

Citations2
Published2011
Admission routes1
Has abstractyes

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