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Record W3000174550 · doi:10.1061/9780784481653.078

Cast Iron Trunk Watermains in the City of Toronto

2018· article· en· W3000174550 on OpenAlexaboutno aff
Jackie Siu

Bibliographic record

VenuePipelines 2018 · 2018
Typearticle
Languageen
FieldEngineering
TopicGeophysical Methods and Applications
Canadian institutionsnot available
Fundersnot available
KeywordsTrunkCast ironComputer scienceHistoryArchaeology

Abstract

fetched live from OpenAlex

Transmission watermains in the city of Toronto use predominantly the following two material types: cast iron with cement mortar lining (“CML”) and CML steel with concrete encasement. The focus of this paper is to discuss the history, current condition, the city’s cast iron watermain rehabilitation plan, and a few recent highlight projects related to cast iron trunk watermain replacement. Record drawings show that cast iron pipes in Toronto’s transmission water system were used from 1900s to early 1950s. After 1950s, cement mortar lined steel pipes with concrete encasement were the predominant standard for new trunkmains. Currently, there are about 100 km of cast iron trunk watermain ranging from 600mm to 900mm in diameter still in service today. Most of them in the older parts of Toronto such as downtown core and west end of downtown Toronto. Currently, the city has planned $153 million (Canadian Dollars—“CAD”) over the next 10 years to replace and rehabilitate cast iron trunkmains to restore the useful life of our trunkmain linear infrastructure.

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)0.000
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.023
Threshold uncertainty score0.112

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0030.001
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0080.000

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 head0.024
GPT teacher head0.283
Teacher spread0.260 · 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

Citations0
Published2018
Admission routes1
Has abstractyes

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