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Record W3048146526 · doi:10.1061/9780784483190.021

Keeping Rain Flowing in the Right Direction: A Stormwater Trunk Sewer Rehabilitation Case Study

2020· article· en· W3048146526 on OpenAlex
Matthew G. Devitt, Jennifer Hale, Oscar A. Orellana, Erez N. Allouche

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenuePipelines 2020 · 2020
Typearticle
Languageen
FieldEnvironmental Science
TopicUrban Stormwater Management Solutions
Canadian institutionsStantec (Canada)Associated Medical Services
Fundersnot available
KeywordsStormwaterRehabilitationStormwater managementTrunkEnvironmental scienceStormHydrology (agriculture)Surface runoffGeologyGeotechnical engineeringMeteorologyGeographyPhysical therapyEcology

Abstract

fetched live from OpenAlex

A 1 km (~3,300 lf) long section of the North York Stormwater Trunk Sewer (STS), a 3,000 mm (118”) diameter corrugated metal pipe that services approximately 800 ha in Toronto, Ontario, has sections that are in poor condition to near failure and needs timely repairs. The culvert was built along an existing tributary to Black Creek, which resulted in a highly irregular alignment, that includes frequent sharp vertical bends and horizontal sweeps. Additionally, the culvert was constructed in several phases over a period of approximately 20 years as a result consists of multi-plate and CMP products lined with various products (tar, shotcrete, and concrete). This paper will summarize the design phase of the project to rehabilitate the deteriorating sewer, which included selecting the optimal rehabilitation methods, and producing designs and implementation plans to suit the site conditions and pipe geometry.

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.

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)0.001
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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.230
Threshold uncertainty score0.932

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.017
GPT teacher head0.245
Teacher spread0.228 · 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