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Record W4367154983 · doi:10.36487/acg_repo/2355_20

Design of a reliable pressure measurement method for paste backfill pipelines

2023· article· en· W4367154983 on OpenAlexaff
Christopher H. T. Lee

Bibliographic record

VenuePaste/˜Pœaste · 2023
Typearticle
Languageen
FieldEngineering
TopicGeotechnical Engineering and Underground Structures
Canadian institutionsBanff CentreGeomechanica (Canada)University of Alberta
Fundersnot available
KeywordsPipeline transportPressure measurementGeotechnical engineeringComputer sciencePetroleum engineeringMaterials scienceGeologyEngineeringMechanical engineering

Abstract

fetched live from OpenAlex

A key component of underground distribution system (UDS) design for paste backfill is a pressure indicating transmitter (PIT), which allows the pressure at the instrument’s location to be measured and used to provide several useful diagnostic functions. These functions include the determination of friction losses and overpressures as well as the detection of pipe breakage or blockage and confirmation of flushing progress. Unfortunately, the usefulness of these PITs can be compromised due to the demanding nature of the application in which the cemented paste will fill up any dead leg in the instrument mounting branch of the pipe spool and prevent pipeline pressures from being accurately measured by the sensor. This requires mounting the PIT close to the pipe’s inside diameter, which can lead to sensor damage. PIT accuracy has been found to be unreliable due to these issues, and PITs frequently require cleaning or replacement. This paper discusses a planned PIT mounting spool design that uses a protective liner on the inside diameter of the pipe. The liner allows pressures to be transmitted to the PIT while avoiding the sensor head damage or dead leg build-up problems that currently make PIT usage unreliable.

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 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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.847
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

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

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 head0.034
GPT teacher head0.247
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 teacher head, not a consensus.

Study designSimulation or modeling
Domainnot available
GenreMethods

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
Published2023
Admission routes1
Has abstractyes

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