The CSIRO In-Situ Laboratory: a field laboratory for derisking underground gas storage
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The industry in western Australia has committed to addressing their carbon emissions in response to the governments aspiration of net zero greenhouse gas emissions by 2050. Natural gas will play an important role in the transition to a fully renewable energy market but will require the geological storage of carbon dioxide to limit emissions and enable the production of blue hydrogen. Underground storage of energy in general (e.g. natural gas, hydrogen, compressed air) will be needed increasingly for providing options for temporary storage of energy from renewable resources and for energy export. Storage operations would need to provide adequate monitoring systems in compliance with yet to be defined regulations and to assure the public that potential leakage or induced seismicity could be confidently detected, managed and remediated. The In-Situ Laboratory in the southwest of western Australia was established in 2019 as a research field site to support low emissions technologies development and provides a unique field site for fluid injection experiments in a fault zone and testing of monitoring technologies between 400 m depth and the ground surface. The site currently consists of three wells instrumented with fibre optics, pressure, temperature and electric resistivity sensors as well as downhole geophones. A controlled release of CO2 and various water injection tests have demonstrated the ability to detect pressure and temperature effects associated with fluid injection. Future experiments planned at the site will help in improving the sensitivity of monitoring technologies and could contribute to defining adequate monitoring requirements for carbon dioxide, hydrogen and other energy storage operations.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it