Discovering Australia’s future petroleum resources: the strategic geoscience information role of government
Bibliographic record
Abstract
Australia’s production of oil is in steady decline. Given the maturity of the oil producing areas, only the discovery of a significant new oil province can arrest the long-term decline in Australian production. Moreover, the increase in demand for clean energy and the location of Australia’s major gas reserves means additional gas resources are also desirable. Many sedimentary basins both onshore and offshore are under-explored and are classed as exploration frontiers. Only Australian national, State and Territory governments, acting jointly or severally in partnership with the private sector, can ensure that the petroleum resource endowment of these frontiers is appropriately explored and developed to the benefit of the nation. As a nation, Australia needs to know the extent of this resource endowment. A major barrier to the exploration of these frontier basins is the absence of sufficient basic geological information to allow exploration investors to make well-informed decisions. Understanding prospectivity is a primary consideration for explorationists, but such assessments are fundamentally dependent upon an infrastructure of geoscience data, concepts and knowledge which provide the framework of successful exploration. The absence of information means high risk and reduces the possibility of investment in exploration in frontier basins. For exploration frontiers the basic geological information collected by State and national geological surveys is fundamental to informed decision-making by exploration companies. Australia competes with other nations for global exploration investment. Given the sovereign rights to the resource and the importance of oil and gas to the nation’s economy and security, provision of pre-competitive geoscience information by government is an effective way of attracting exploration investment to Australia. However, the supply of pre-competitive geoscience data—which includes ready access to pre-existing industry data and information—is a strategic enterprise that must be maintained for many years if it is to serve the needs of the nation and the industry through the long lead and cycle times inherent in the exploration and production cycle. Promotion of successful exploration is dependent on the maintenance of a competitive exploration environment that includes the free flow of relevant information in forms that meet the need of all market players. Australia has excellent examples of strategies and case histories where provision and promotion of geoscience information has been effective in attracting significant exploration investment.
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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 | 0.006 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.006 | 0.006 |
| Scholarly communication | 0.012 | 0.011 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.006 | 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".