Assessing the effectiveness of ocean alkalinity enhancement on carbon sequestration and ocean acidification
Bibliographic record
Abstract
As atmospheric carbon dioxide (CO 2 ) levels continue to rise, increasing attention is focussed mitigation techniques that could enhance the natural draw down of CO 2 . One such mitigative intervention is ocean alkalinity enhancement (OAE). OAE involves dissolving alkaline materials into ocean surface waters to increase its natural CO 2 buffering capacity. Limestone and lime have received the most attention given their widespread availability. Here, we address the order one policy-relevant question of whether OAE represents a viable CO 2 removal solution to global warming. We use the UVic Earth System Climate Model to explore the potential of OAE interventions under representative concentration pathways (RCPs) 2.6, 4.5, 6.0, and 8.5. For each RCP, we undertake three OAE interventions. First, we assume that the global annual production of limestone is crushed and uniformly distributed across and immediately disassociates in the surface waters of the global ocean. Second, we assume that the global production of limestone is converted to lime with the CO 2 released in this process being added to the atmosphere. In the third intervention, we repeat the second intervention but sequester the CO 2 arising from lime production. Our results suggest that CaCO 3 -based OAE interventions have little potential for mitigating global warming given the restraint of the current world’s output of limestone from mining.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".