Assurance monitoring approach for the Heartland Area Redwater Project (HARP) geological CO2 storage project, Alberta, Canada
Bibliographic record
Abstract
The Heartland Area Redwater Project (HARP) aims to inject and geologically store up to a gigatonne of captured CO 2 derived from Alberta’s Industrial Heartland. The storage formation will be the vast water leg of the Devonian Redwater-Leduc carbonate reef structure, also host to Canada’s third largest conventional oil pool. Extensive characterization activities, including geological, geophysical, hydrogeological, geochemical, soil, atmospheric studies and land management practices are currently nearing completion. Combined information from these studies is allowing for the integration and development of conceptual and numerical models. Intensive activity is currently focused upon a small pilot site within the water leg of the reef. Should this pilot program prove to be successful, CO 2 injection, and the associated monitoring program, may be scaled up to allow an annual injection of up to 1 million tonnes of CO 2 . The integration of near surface characterization and monitoring along with injection formation characterization (e.g. fluid sampling and time-series geophysics) and well integrity studies have guided the design and implementation of the HARP assurance monitoring program. Field data acquisition and interpretation are currently ongoing to allow a preoperational phase, pilot-scale, assurance monitoring baseline to be established. Public consultation and stakeholder involvement with the project forms a key component in both implementing the program, and allowing for sustainable agricultural and industrial development within the Alberta Industrial Heartland area.
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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 | 0.002 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".