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Record W2886580249 · doi:10.1139/cjce-2018-0057

Improving the geometry of manholes designed for separate sewer systems

2018· article· en· W2886580249 on OpenAlexvenueno aff
Alaa Abbas, Felicite Ruddock, Rafid Alkhaddar, Glynn Rothwell, Robert Andoh

Bibliographic record

VenueCanadian Journal of Civil Engineering · 2018
Typearticle
Languageen
FieldEngineering
TopicGeotechnical Engineering and Underground Structures
Canadian institutionsnot available
FundersLiverpool John Moores University
KeywordsEngineeringFlow (mathematics)Civil engineeringGeotechnical engineeringStructural engineeringForensic engineeringMathematics

Abstract

fetched live from OpenAlex

The design of manholes dates back more than 100 years. However, there have been developments such as the use of new materials for the manufacture of manholes, and advances in inspection and maintenance technologies, allowing improvements to the shape of manholes. This paper presents an innovative design for manholes, created to overcome the challenges associated with the installation of separate sewer systems in narrow streets, common to both UK and EU cities. The traditional separate sewer system has two separate manholes. The proposed manhole combines these two manholes into one structure, with two separate chambers, to allow storm flow and foul flow to pass through the same manhole without mixing. The structural performance of the new design has been tested using mathematical modelling validated by experimental tests. The results are compared with the structural performance of traditional manholes. The new design shows an improved resistance to high live loads.

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)0.002
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: none
Teacher disagreement score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.007
GPT teacher head0.181
Teacher spread0.174 · 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

Citations12
Published2018
Admission routes1
Has abstractyes

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