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Record W2536309004 · doi:10.1115/ipc2000-178

Innovative Project Management Techniques: Major International Pipeline Project

2000· article· en· W2536309004 on OpenAlexaff
Robert G. Marshall, Robert Galatiuk, Michal Mensik

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicOffshore Engineering and Technologies
Canadian institutionsTransCanada (Canada)
Fundersnot available
KeywordsScheduleProject managementContext (archaeology)ProcurementProject planningProject management triangleProject charterPipeline (software)Computer scienceOperations researchEnvironmental resource managementEngineering managementBusinessEngineeringSystems engineeringEnvironmental scienceGeology

Abstract

fetched live from OpenAlex

The Gasoducto del Pacifico Pipeline Project (GasPacifico), a 543 Km. pipeline transporting gas from the Province of Neuquen in Argentina to major cities in Chile, was accomplished in record time and under budget. The project was executed in a time frame even shorter than a previous fast track project in the region, the GasAndes Pipeline Project which also crossed the imposing Andes mountain range. Relying on the experience of the GasAndes Project, the Project Management Team, achieved success through the innovative implementation of project management techniques tailored to the specific challenges of the GasPacifico Project which include: - The fast track nature of the project; - Contractual obligations imposed by the Project Management Agreement between TransCanada International (TCI) and the owner, GasPacifico; - Environmental contraints (route traversed a national park in Chile and areas of high erosion and instability); - Seasonal constraints (one summer of construction, heavy rains in winter); - Two countries with two sets of laws and stringent regulatory regimes; - Procurement and importation of major equipment, materials and pipe. The project management techniques balanced the triumvirate of quality, schedule and cost while managing the Owner’s risks within the boundary constraints of: - Schedule commitments; - Budget; - Right-of-way acquisition; - Regulatory Permits; - Design challenges; - Procurement limitations; - Environment requirements; - Construction challenges. This paper presents the project management techniques used to manage these challenges, placing them in a relevant context, with the intent that learnings can be applied to other international projects.

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)0.006
metaresearch head score (Gemma)0.007
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: Empirical · Consensus signal: none
Teacher disagreement score0.006
Threshold uncertainty score0.032

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.007
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0020.001
Scholarly communication0.0020.002
Open science0.0010.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0060.002

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 head0.009
GPT teacher head0.239
Teacher spread0.230 · 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
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
Published2000
Admission routes1
Has abstractyes

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