Advancing CCS Monitoring Technologies at the Carbon Management Canada’s CaMI Field Research Station
Bibliographic record
Abstract
Summary Carbon Capture and Storage (CCS) is crucial in reducing GHG emissions and is now fully integrated into the net-zero scenarios. Requirements for a proper CCS Monitoring, Measurement, and Verification (MMV) plan include a timely warning of containment or conformance anomalies and the use of the best available technologies that are economically achievable and based on sound science. Research pilot sites are crucial in developing monitoring technologies to de-risk CCS and develop MMV plans. The CMC-CaMI Field Research Station (in Newell County, Alberta, Canada) is a well-established site where a broad range of monitoring is tested on a gas-phase CO2 leakage scenario. Detection thresholds for different technologies have been determined: 10 tonnes for electrical resistivity tomography and 34 tonnes for time-lapse vertical seismic profile. The site is also used to develop new techniques, such as sparse seismic monitoring with permanent sources mounted on screw pile and permanently installed borehole receivers in an observation well. Initial results are promising, with a high repeatability and a strong attenuation of the near-surface unconsolidated layer effects. This innovative method will lead to continuous reservoir surveillance, a crucial parameter for adequate CCS MMV plans.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".