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Record W2001326989 · doi:10.2118/0805-0026-jpt

Q&A with Linda Cook

2005· article· en· W2001326989 on OpenAlexaboutno aff
John Donnelly

Bibliographic record

VenueJournal of Petroleum Technology · 2005
Typearticle
Languageen
FieldEngineering
TopicReservoir Engineering and Simulation Methods
Canadian institutionsnot available
Fundersnot available
KeywordsNatural gasLiquefied natural gasChief executive officerPetroleum industryPetroleumEngineeringPaceMiddle EastManagementNatural resource economicsEconomicsPolitical scienceWaste managementGeographyEnvironmental engineeringGeologyLaw

Abstract

fetched live from OpenAlex

Linda Cook, SPE, is Executive Director, Gas and Power, of the Royal Dutch/Shell Group. She is also responsible for Renewables and Hydrogen, Shell Global Solutions, Group Research, East Asia and Australasia. She has been a managing director of Royal Dutch Petroleum Co. since August 2004. She joined Shell after receiving a BS degree in petroleum engineering from the U. of Kansas and has worked for Shell companies in the U.S., The Netherlands, and Canada. Before becoming a group managing director, Cook was President and Chief Executive Officer of Shell Canada Ltd. She also is a Director of the Boeing Co. Demand for natural gas is surging, and gas may soon replace oil as the hydrocarbon of choice. Will the energy industry be able to keep up with the global demand? What are the challenges? Indeed, natural gas demand is surging. In fact, gas demand will grow more rapidly than oil, and the latest Shell scenarios suggest that by 2025, natural gas will meet more than 25% of the world’s energy needs. The industry is doing what it can to enable supply to keep pace with the rising demand. In general, capital budgets are increasing, as is the hiring of new graduates. For natural gas in particular, major new projects are being fast-tracked to production, especially in the area of liquefied natural gas (LNG) in a number of countries including Qatar, Russia, Nigeria, and Australia. But the challenges are significant. The engineering and construction sectors are stretched to the limit of their capabilities. Delivery times for long-lead-time equipment are lengthening. Logistics are strained in some areas. And there is a shortage of the human resources required in certain disciplines to meet the surge in natural gas demand.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.832
Threshold uncertainty score0.348

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
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 head0.010
GPT teacher head0.248
Teacher spread0.238 · 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 designSimulation or modeling
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
Published2005
Admission routes1
Has abstractyes

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