Enhanced chemical weathering as a geoengineering strategy to reduce atmospheric carbon dioxide, supply nutrients, and mitigate ocean acidification
Bibliographic record
Abstract
Abstract Chemical weathering is an integral part of both the rock and carbon cycles and is being affected by changes in land use, particularly as a result of agricultural practices such as tilling, mineral fertilization, or liming to adjust soil pH. These human activities have already altered the terrestrial chemical cycles and land‐ocean flux of major elements, although the extent remains difficult to quantify. When deployed on a grand scale, Enhanced Weathering (a form of mineral fertilization), the application of finely ground minerals over the land surface, could be used to remove CO 2 from the atmosphere. The release of cations during the dissolution of such silicate minerals would convert dissolved CO 2 to bicarbonate, increasing the alkalinity and pH of natural waters. Some products of mineral dissolution would precipitate in soils or be taken up by ecosystems, but a significant portion would be transported to the coastal zone and the open ocean, where the increase in alkalinity would partially counteract “ocean acidification” associated with the current marked increase in atmospheric CO 2 . Other elements released during this mineral dissolution, like Si, P, or K, could stimulate biological productivity, further helping to remove CO 2 from the atmosphere. On land, the terrestrial carbon pool would likely increase in response to Enhanced Weathering in areas where ecosystem growth rates are currently limited by one of the nutrients that would be released during mineral dissolution. In the ocean, the biological carbon pumps (which export organic matter and CaCO 3 to the deep ocean) may be altered by the resulting influx of nutrients and alkalinity to the ocean. This review merges current interdisciplinary knowledge about Enhanced Weathering, the processes involved, and the applicability as well as some of the consequences and risks of applying the method.
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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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".