Causal networks as a tool to assess environmental risk of CO2 leakage
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
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 | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".