Australia: Energy Superpower of the Low-Carbon World
Bibliographic record
Abstract
Modern economic development over the past quarter of a millennium has transformed for the better the lives of most people. It has lifted about a third of humanity to standards of comfort, knowledge, health and longevity unknown to the elites of earlier times. It has placed another half of the people on earth on paths towards enjoying those living standards comfortably within this century, so long as development is not blocked by a breakdown in political or ecological order. The remaining sixth of humanity aspires to be on one of those paths and there will be no stable resting place for humanity until they have achieved that goal. Modern economic development was built on intensive use of fossil fuels. Solar energy had been converted by photosynthesis and natural storage processes into coal, oil and gas over hundreds of millions of years. It was then drawn down at a much faster rate than it was ever deposited, to drive the machines of the newly industrial world and meet the expanding demands of the households enriched by economic growth. Coal dominated the mix of fossil fuels at first, and was joined by oil from early and natural gas from late in the twentieth century. The availability of this concentrated energy was important to the burst of incomes growth that revealed the tendency for human fertility to fall when living standards became higher and more secure. The decline in fertility, in
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.062 | 0.008 |
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".