Bibliographic record
Abstract
Carbon capture and storage (CCS) has been identified as a priority issue within the context of the North American Climate Change and Energy Collaboration and Mission Innovation. An important aspect of CCS is the need to improve public confidence in long-term geological storage of CO2. A key to developing confidence for the longer term is a demonstration of safe and expected storage behaviourin the short term. Two primary concerns of the public and government regulatory bodies are the potential for induced seismicity and for CO2leakage. To alleviate these concerns, storage monitoring is critical in demonstrating that the subsurface CO2plume is behaving as expected, and that induced microseismicor seismic activity is being closely monitored. The AquistoreCO2Storage Project is a multi-year research and monitoring project to demonstrate that storing CO2deep underground is a safe and workable solution to help reduce greenhouse gas emissions to the atmosphere. The Geological Survey of Canada's studies within the project are focused on the development of improved monitoring methodologies and a better understanding of the relationship between CO2 injection and induced seismicity. A total of ~200 ktonnesof CO2 were injected at the Aquistoresite from April-2015 to May-2019. Injection is occurring within a saline formation at a depth of 3150-3350 m. Passive seismic monitoring at the site which began in 2012 has not identified any seismicity associated with the injection process. The first time-lapse 3D seismic surveys were conducted in February and November of 2016 when the cumulative injected quantity of CO2 was 36 ktonnesand 102 ktonnes, respectively. The latest 3D survey occurred in March-2018 with 141 ktonnesinjected. The resultant time-lapse seismic images show how the CO2 plume is partitioned vertically within the reservoir and how it is spreading laterally. The seismic observations indicate that the initial geological model used for CO2 flow simulations will have to be modified.
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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 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".