Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.010 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.682 | 0.488 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".