Conceptual Models for CO<sub>2</sub> Release and Risk Assessment: a Review
Bibliographic record
Abstract
Carbon capture and storage (CCS) is a technology for mitigating the contribution of fossil fuel emissions to global warming. The technology is based on capturing carbon dioxide (CO2) from point sources and storing it in geological formations in such a way that is does not enter the atmosphere. CCS requires the transport of CO2 from source to sink. This can involve one or a combination of transport media: truck, train, ship or pipeline. Transport by pipeline is the preferred option for transporting large quantities of CO2 over long distances. The majority of CO2 pipelines are in the USA and Canada, along with substantial in-field pipework for Enhanced Oil Recovery (EOR) projects (Kelliher et al, 2009, Kadnar, 2008). The USA experience cannot be easily applied to other regions or situations, because the CO2 pipelines are located in areas with low population density. In general, as stated in the report of the IPCC on CCS (IPCC, 2005), there is a lack of knowledge regarding the safety of pipeline transmission of CO2 in densely populated areas. The aim of this paper is to review the current state of the art in the analysis of risk for CO2 transport by pipeline. A brief review is presented of current models for CO2 release, the assessment of impact from such release, and overall risk analysis. For a simple case study, a comparative analysis is presented of alternative models for the calculation of consequences. This comparison indicates that different assumption models and software lead to important differences in the calculation of consequences. One of the problems is the difficulty in comparing and assessing results due to lack of experimental data. Key unresolved problems and some directions for research needed are identified.
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.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".