Seafloor alkalinity enhancement as a carbon dioxide removal strategy in the Baltic Sea
Bibliographic record
Abstract
Abstract Carbon dioxide removal from the atmosphere and storage over long times scales in terrestrial and marine reservoirs is urgently needed to limit global warming and for sustainable management of the global carbon cycle. Ocean alkalinity enhancement by the artificial addition of carbonate minerals to the seafloor has been proposed as a method to sequester atmospheric CO2 and store it in the ocean as dissolved bicarbonate. Here, a reaction-transport model is used to scrutinize the efficacy of calcite addition and dissolution at a well-studied site in the southwestern Baltic Sea – a brackish coastal water body in northern Europe. We find that most calcite is simply buried without dissolution under moderate addition rates. Applying the model to other sites in the Baltic Sea suggests that dissolution rates and efficiencies are higher in areas with low salinity and undersaturated bottom waters. A simple box model predicts a tentative net CO2 uptake rate from the atmosphere of 3.2 megatonnes of carbon dioxide per year for the wider Baltic Sea after continually adding calcite to muddy sediments for 10 years. More robust estimates now require validation by field studies.
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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) | 0.000 | 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".