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Record W2122605868 · doi:10.1115/ipc2006-10339

Scripting Real-Time Application Platform (RTAP) Panels: An Overview of the Terminal Swing Panel Project

2006· article· en· W2122605868 on OpenAlexaboutno aff
Chris Lewis

Bibliographic record

VenueVolume 3: Materials and Joining; Pipeline Automation and Measurement; Risk and Reliability, Parts A and B · 2006
Typearticle
Languageen
FieldEngineering
TopicOil and Gas Production Techniques
Canadian institutionsnot available
Fundersnot available
KeywordsSwingComputer scienceTerminal (telecommunication)Scripting languageEmbedded systemXMLPipeline transportPayload (computing)USableGraphical user interfaceOperating systemReal-time computingEngineeringComputer networkWorld Wide Web

Abstract

fetched live from OpenAlex

Enbridge Pipelines remotely controls the flow of approximately two-thirds of Canada’s crude oil and refined products produced from the Western Canadian Sedimentary basin through the Enbridge mainline pipeline system. These various products divided into batches run though multiple terminal systems. Each terminal site is unique and requires a multitude of specialized control systems to maintain its various flow patterns. To efficiently control these terminals Enbridge operators use the custom built PROCYS SCADA system. This system contains a graphical user interface containing schematics and panels that allow operator view and control. As part of this system a subset of control panels were developed and labeled swing panels. Swing panels allow operators to safely and efficiency run product through the various systems within a terminal. Swing panels have been in successful operation for over 10 years, however their development and maintenance cycles were less than optimal for operators and SCADA support staff. For this reason the Enbridge Terminal swing panel system was redesigned. These second generation swing panels were designed to use XML input files and Perl scripting to allow for automated construction. By encapsulating terminal swings in XML the swing panels are easier to maintain and build. This re-design resulted in a system of more functional, usable, and maintainable swing panels. The following paper will discuss this project in more detail.

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)0.003
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.006
Threshold uncertainty score0.021

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.003
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0020.002
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0060.004

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 head0.045
GPT teacher head0.257
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 designNot applicable
Domainnot available
GenreOther

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
Published2006
Admission routes1
Has abstractyes

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Same venueVolume 3: Materials and Joining; Pipeline Automation and Measurement; Risk and Reliability, Parts A and BSame topicOil and Gas Production TechniquesFrench-language works237,207