Session 20. Oral Presentation for: Causal networks as a tool to assess environmental risk of CO2 leakage
Bibliographic record
Abstract
Presented on 28 May 2025: Session 20 The role of carbon capture and storage (CCS) as a quantitatively significant emissions reduction tool for Australia and globally is acknowledged by many. One perceived challenge that CCS faces relates to the potential for adverse environmental impacts due to the risk of leakage. CSIRO has completed a study, commissioned by the International Energy Agency’s Greenhouse Gas R&D Programme (IEAGHG), using causal networks a framework allowing identification of steps which provide insight into cause-and-effect relationships. The network describes a series of drivers, activities and stressors that could be at play, generically speaking, during geological carbon storage together with processes that may arise if leakage were to occur where endpoints or impacts are described. An assessment of the risk, its likelihood and consequences are explained and supported by a range of peer-reviewed resources to illustrate actual impacts. Case studies, analogues and pilot tests have been instrumental in informing risks and consequences. The report demonstrates that while leakage (i.e. via compromised well bores or unforeseen geological conditions) could be material in rare circumstances, environmental impacts via pathways from stressors to endpoints (e.g. air quality, marine biodiversity) are low. If appropriate geological appraisal is conducted, the geological leakage risk remains low, and mitigation steps can be used to manage remaining risks and impacts of CCS. To access the Oral Presentation click the link below. To read the full paper click here
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.646 | 0.267 |
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".