Prime Movers of Globalization: The History and Impact of Diesel Engines and Gas Turbines
Bibliographic record
Abstract
Review: Prime Movers of Globalization: The History and Impact of Diesel Engines and Gas Turbines By Vaclav Smil Reviewed by Byron Anderson DeKalb, Illinois, USA Smil, Vaclav. Prime Movers of Globalization: The History and Impact of Diesel Engines and Gas Turbines. Cambridge, MA: The MIT Press, 2010. 261 pp. ISBN: 9780262014434, US $29.95, cloth. Prime Movers of Globalization is about the history and impact of the internal combustion engine invented by Rudolf Diesel in the 1890s and the gas turbine invented by Frank Whittle and Hans-Joachin Pabst von Ohain in the 1930s. Diesel’s engines evolved to power ships, railroads, trucks and generators, and gas turbines evolved to power jet airplanes. Gas turbines and diesel engines eventually became indispensible technologies to worldwide trade and transportation, yet are commonly overlooked as movers of the global economy. The book will help correct this gap. Smil, Distinguished Professor at the University of Manitoba and author of many books on energy, ecology and globalization, describes development of the engines, including the inventions that led to the diesel and turbine engines and the subsequent alterations, advancements and transformations made to the engines. These would include key inventors, for example, the gas-fueled engines of Nicolaus Otto, Gottlieb Daimler, and Karl Benz, and companies that furthered turbine technology through research, such as General Electric, Rolls Royce, and Pratt and Whitney. Clearly, Smil admires what it took to develop these massive engines, though the associated problems are also mentioned, for example, job losses incurred by the move of manufacturing plants to overseas locations made possible by large bulk carriers. While categorized as a book of history and globalization, environmental issues are included, for example, greenhouse gas-emissions from ships, trucks and aircraft, and the global spread of microbes and invertebrate species in ballast waste of commercial ships. Surprisingly, diesel engines and gas turbines are the most efficient machines ever for moving bulk cargo long-distances, and “maritime shipments have the lowest carbon dioxide emission rates when compared to other modes of cargo traffic in terms of per tons kilometer…” (p. 162). No practical replacements will challenge these movers anytime soon, and Smil believes that in the long run, there will always be more globalization, not less. The ecological footprint of diesel engines and gas turbines has been continuously improved by technology, and new devices and procedures under development should provide further efficiencies. Interestingly, prime movers have probably reached their maximum size, and making ships or jets larger or faster than what exist today will cause them lose their efficiency and economy of scale. This book is not casual reading. The level of technical detail may challenge some, though most interested readers should come away with a deeper appreciation of the history and technology. The text is nicely supplemented graphs, charts, maps and pictures. The book concludes with a useful glossary of measurement units, references, and name and subject indices. Recommended for academic libraries. Byron Anderson, , retired from Northern Illinois University Libraries, DeKalb, IL 60115, USA. Electronic Green Journal, Issue 32, Fall 2011, ISSN:1076-7975
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 0.005 |
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".