Direct Aquatic Application of Crushed Dolomite Reduces CO2 Evasion in an Acidified River
Bibliographic record
Abstract
Acidified rivers may have increased CO2 emissions because their low pH transforms inorganic carbon in the form of bicarbonate anions to CO2, which can evade to the atmosphere, thus interrupting the delivery inorganic carbon to the oceans, a key flux in the long-term carbonate silicate cycle. Enhanced weathering (EW) is a carbon dioxide removal (CDR) strategy aiming to increase drawdown of atmospheric CO2 through accelerated carbonation weathering of crushed minerals with targeted carbonate sequestration in oceanic stores. To date, EW research has been focused on terrestrial application of crushed minerals, and the CDR capability of enhancing weathering via addition of crushed minerals to rivers from lime dosers is essentially unexplored. Lime dosers have been used for decades to directly deposit crushed carbonate rock to rivers as a function of river flow in Norway and Nova Scotia, Canada, yet their potential as a CDR tool has yet to be verified in the field. In this study, we adapt CO2 flux sensors (eosFD) designed for soils to be deployed in rivers. We conducted field trials on the Killag River, Nova Scotia, upstream and downstream of a lime doser over a period of six weeks in the autumn of 2020. Preliminary analysis shows elevated CO2 evasion rates upstream of the lime doser and decreased evasion rates downstream. Aside from flood waves, CO2 evasion at the downstream (treated) site is reduced to almost zero for extended periods of time. Next steps are to identify whether the reduced CO2 evasion is due to CO2 drawdown via increased carbonation weathering of the crushed dolomite or through reduced CO2 evasion due to increased pH, or from a combination of the two processes. The results of this study may have implications for carbon credit programs for acidification mitigation and may encourage more widespread use of enhanced weathering as a CDR tool in rivers.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".