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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 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)0.000
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: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.889
Threshold uncertainty score0.560

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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)0.0000.000

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 head0.009
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 teacher head, 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
Published2000
Admission routes1
Has abstractyes

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