Bibliographic record
Abstract
B ooks on the global energy system are replete with utopian visions: the nuclear future, the solar future, the efficiency future, the hydrogen future, and the “small-scale energy technologies are beautiful” future. All too often, these visions are described in detail, but the path for getting there is left vague. This reminds me of the cartoon of two mathematicians at the blackboard gazing in satisfaction at a complex set of equations on the left, the simple elegant solution on the right, and an incomprehensible jumble of equations and symbols in the middle to link both sides, all of which are crossed out except for the statement “somewhere about here a miracle happens.” I too have presented a vision of a future, desirable energy system. In developing this vision, however, I have taken into account key real-world constraints on the potential for shifting away from current trends. These include the strong penchant for humans to use substantially more energy as population and wealth increases, public attitudes to extreme event risks that are especially challenging for nuclear power, wide-ranging concerns for the geopolitical risks associated with oil import dependence or the global spread of nuclear weapons, the difficulties of attaining a rapid scale-up of modern renewables-based technologies, and the path dependence advantages of fossil fuels. In combining these constraints in a choice evaluation that involves both prescription and prediction, I have outlined the energy forms, technologies, costs and international developments for achieving a more sustainable energy path.
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 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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.003 | 0.009 |
| Scholarly communication | 0.015 | 0.029 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.007 | 0.010 |
| Insufficient payload (model declined to judge) | 0.028 | 0.011 |
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".