Evaluation of the potential of glauconite in the Western Canadian Sedimentary Basin for large-scale carbon dioxide mineralization
Bibliographic record
Abstract
Geologic carbon dioxide (CO 2 ) storage is an essential and economical measure to mitigate global climate change. CO 2 storage potential in sedimentary reservoirs is commonly passed over in favor of more reactive rock formations, such as basalts, because the latter offer rapid, permanent storage as carbonate minerals while the former are thought to only offer less permanent physical trapping of CO 2 . Nevertheless, recent research has demonstrated that carbonation reactions in glauconitic sandstones are favorable under realistic reservoir conditions, although the overall availability of glauconite for carbonation has not yet been quantified. Here, we use calculations based on 11,652 well logs to show that glauconitic sandstones offer significant and previously overlooked potential for sedimentary reservoir-based mineral carbonation. Our results demonstrate that hundreds of gigatons of CO 2 could be sequestered by carbonating the immense quantity of glauconite underlying Alberta, Canada alone. Importantly, these glauconitic sandstones, and others worldwide, have long been exploited for their favorable hydrocarbon production capacity. Thus, global societies eager to limit greenhouse gas emissions may need to look no further than the reservoirs they are already exploiting. Moreover, because the requisite injection infrastructure is oftentimes still active, glauconitic sandstones may present the highest priority, lowest capital cost substrate for mineral carbonation worldwide.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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 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".