Little Green Men: The Growth of Sustainable Warfare in the United States of America
Bibliographic record
Abstract
Since the first days of President Barack Obama's administration, the U.S military has embarked on an ambitious plan to change the way its fighting forces-Army, Navy, Marine Corps and Air Force-fight and fuel wars.Since 2009, the U.S. Department of Defense has taken great strides to incorporate renewable energy technology into its infrastructure and operations.It has also made significant gains in improving the efficiency with which fossil fuels are still used.As the most technologically advanced military in the world, the armed forces of the United States are well ahead of all other nations when it comes to adopting and advancing renewable energy in warfare.War has always driven the most important technological advances in human history.From agriculture to industry, medicine to commerce, through to the modern artificial intelligence and automation industries, technological breakthroughs are often hatched inside military research laboratories, tested on the battlefield and are later adopted for broad civilian use.Whether these technologies are developed first for the military-as in the harnessing of atomic energy, the development of the Internet, drone technology, digital photography and GPS mapping-or first in the civilian marketplace before military absorption and amplification, is not a central question of this study.That is to say that whether the military spearheads a technology's creation or is the thrust behind the spear matters little.The considerable capital, research and development, manpower and infrastructural investments that the U.S. Department of Defense affords technologies deemed useful to warfare is here the key difference-maker in a technology's
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
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".