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Record W2998952224 · doi:10.1061/9780784482490.024

Mitigation of Issues Raised during Construction of Trunk Sewer Rehabilitation Project

2019· article· en· W2998952224 on OpenAlexaff
Chris Martire, Mudassar Muhammad, Birju Shah

Bibliographic record

VenuePipelines 2019 · 2019
Typearticle
Languageen
FieldEngineering
TopicUnderground infrastructure and sustainability
Canadian institutionsRegional Municipality of DurhamHatch (Canada)
Fundersnot available
KeywordsRehabilitationConstruction engineeringComputer scienceTrunkEngineeringCivil engineeringPhysical therapyMedicine

Abstract

fetched live from OpenAlex

This paper presents the approach, methods, and challenges for undertaking repairs to York Region’s sanitary sewers. The Regional Municipality of York (York Region) carried out detailed closed-circuit television (CCTV) inspections on the York-Durham Sewage System (YDSS) to assess the rate of inflow and infiltration and to identify sewer sections in need of immediate repair. Based on the inspections, deficiencies were identified at two (2) maintenance holes and fourteen (14) sewer sections that include minor cracks, infiltration, encrustation, and roots and debris in the system. The diameter of the sewers ranged from 20 to 54 inches (525 to 1,350 mm), with a total distance of 1.2 miles (1.8 km). The rehabilitation consisted of spray applied liners for the maintenance holes and installation of localized chemical grout to the sewer sections. These locations were distributed across York Region in three separate cities, with the location of the proposed rehabilitation work occurred within flood plains, high traffic, and environmentally sensitive areas. York Region, Hatch, and associated contractors collectively worked together to mitigate and resolve project challenges and completed the rehabilitation of the YDSS within the prescribed construction timeline.

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)0.004
metaresearch head score (Gemma)0.005
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.022
Threshold uncertainty score0.044

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.005
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0040.001
Scholarly communication0.0030.001
Open science0.0010.002
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.002
GPT teacher head0.215
Teacher spread0.213 · 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
Published2019
Admission routes1
Has abstractyes

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