Developing Australia’s underground hydrogen storage through demonstration
Bibliographic record
Abstract
For Australia to capitalise on the growing hydrogen (H2) economy, the current capability gap for large-scale, secure and cost-effective H2 storage must be addressed. Large-scale underground hydrogen storage (UHS) in porous reservoirs offers the required capacity to balance discrepancies between demand and supply over seasonal durations, and support decarbonisation and security in Australia’s energy system. This becomes essential for export and domestic markets from 2030 onwards. UHS in depleted gas fields can address the infrastructure and safety challenges, as well as supporting long-term supply security while decreasing delivery price through economies of scale. However, the technical readiness for Australia’s industries to undertake UHS is low, with scientific challenges around how stored H2 interacts with subsurface rocks and fluids, and how this impacts the storage efficiency. A commercially relevant demonstration of UHS is essential for providing the knowledge and confidence for large investment into commercial scale UHS. CO2CRC and CSIRO are collaboratively developing such a demonstration, utilising data and learnings from the Otway International Test Centre, as a proxy for commercial-scale UHS operations.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 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".