Mitigating the atmospheric CO<sub>2</sub> increase and ocean acidification by adding limestone powder to upwelling regions
Bibliographic record
Abstract
The feasibility of enhancing the absorption of CO 2 from the atmosphere by adding calcium carbonate (CaCO 3 ) powder to the ocean and of partially reversing the acidification of the ocean and the decrease in calcite supersaturation resulting from the absorption of anthropogenic CO 2 is investigated. CaCO 3 could be added to the surface layer in regions where the depth of the boundary between supersaturated and unsaturated water is relatively shallow (250–500 m) and where the upwelling velocity is large (30–300 m a −1 ). The CaCO 3 would dissolve within a few 100 m depth below the saturation horizon, and the dissolution products would enter the mixed layer within a few years to decades, facilitating further absorption of CO 2 from the atmosphere. This absorption of CO 2 would largely offset the increase in mixed layer pH and carbonate supersaturation resulting from the upwelling of dissolved limestone powder. However, if done on a large scale, the reduction in atmospheric CO 2 due to absorption of CO 2 by the ocean would reduce the amount of CO 2 that needs to be absorbed by the mixed layer, thereby allowing a larger net increase in pH and in supersaturation in the regions receiving CaCO 3 . At the same time, the reduction in atmospheric p CO 2 would cause outgassing of CO 2 from ocean regions not subject to addition of CaCO 3 , thereby increasing the pH and supersaturation in these regions as well. Geographically optimal application of 4 billion t of CaCO 3 a −1 (0.48 Gt C a −1 ) could induce absorption of atmospheric CO 2 at a rate of 600 Mt CO 2 a −1 after 50 years, 900 Mt CO 2 a −1 after 100 years, and 1050 Mt CO 2 a −1 after 200 years.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".