Ensuring domestic supplies of natural gas for Australian businesses and households
Bibliographic record
Abstract
Australian gas markets are undergoing a substantial transformation. The development of LNG has resulted in a step-change in gas demand. The lumpy and capital-intensive nature of exporting gas has, however, shifted the natural incentives of some economic participants. These changed incentives have created some concern among large domestic industrial users of natural gas. Some domestic gas users have advocated for the reservation of gas reserves for domestic consumption. This paper assesses whether the reservation of gas is the best public policy response to the issues facing industrial users. Developing new dispersed supplies of natural gas (e.g., NSW CSG) is the most logical way to reduce pricing pressures for industrial users of natural gas. In this context, the public policy interests of domestic gas producers and consumers should be aligned. Public policy makers must remove unnecessary barriers to the exploration and production of new gas reserves. Increasing supply at a time when new LNG loads (beyond those under construction) are unlikely to materialise would alleviate any potential shortages of natural gas. At the same time, domestic suppliers of natural gas must continue to innovate to manage uncertainties on behalf of their customers. This is already occurring through the development of gas storage to manage peak loads.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 0.002 |
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".