Bibliographic record
Abstract
Energy is undergoing the most significant transition since the alternating current – allowing energy to be generated in large, centralised power stations and safely sent to homes and businesses via thousands of kilometres of high voltage wires – was invented nearly 150 years ago. Energy is increasingly decentralised and low emissions – in Australia, renewables will double from 15 TWh today to 30 TWh by the end of this year. Globally, we are also seeing a major shift. The International Energy Agency forecasts that global population is set to increase by 1.7 billion by 2040, which will see demand for energy rise by about a quarter. This will be driven by the emerging economies of Asia, which are commendably tackling emissions far earlier in their history than today’s established economies. Gas is the key to managing the transition at least cost and least impact to reliability – it is more flexible and able to step in quickly when renewables aren’t generating. Renewables will grow to 40 per cent of the global energy mix under the IEA’s new policies scenario and gas will overtake coal by 2030 to be the second largest source of energy after oil to support this. For Australia, which became the world’s largest exporter of LNG this year, the opportunity to facilitate the global shift to lower emissions as well as maintain a competitive price for domestic users is clear, but depends on policy continuing to support the development of gas resources. With unconventional gas set to become increasingly important in meeting global energy demand, it is also time for the gas industry to step up and ensure that gas is seen as nation building for the Australian economy as coal was in the 20th century. To view the video, click the link on the right.
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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.009 |
| Scholarly communication | 0.006 | 0.011 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.014 | 0.004 |
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".