Maximizing the Detectability of Ocean Alkalinity Enhancement (OAE) While Minimizing Its Exposure Risks: Insights From a Numerical Study
Bibliographic record
Abstract
Abstract Ocean alkalinity enhancement (OAE) can potentially remove gigatons of CO 2 from the atmosphere for durable storage in the ocean. Before implementing OAE at climate‐relevant scales, questions about its safety and verifiability must be addressed. Operational deployment poses a dilemma between pursuing large detectability, essential for effective monitoring, reporting, and verification, and ensuring environmental safety and satisfying regulatory requirements. In this study, we present a computationally efficient approach, based on a high‐resolution, coupled circulation‐dissolution model of Halifax Harbor, to simulating the addition, transportation, dissolution, and sinking of various theoretical alkaline feedstocks for different dosages, seasons, and addition sites. Detectability and exposure risk of OAE are quantified and an approach for optimizing OAE deployment is demonstrated. Mean residence times (MRT) are calculated for different subregions and seasons. Results show that for a given amount of feedstock, summer is more favorable from the perspective of detectability but also creates higher exposure risks than other seasons because of a longer MRT. The exposure risk can be mitigated while maintaining large detectability by choosing optimal feedstocks with different characteristics for different seasons. The exposure risk can also be reduced by spreading alkalinity over multiple addition sites. The optimum allocation, where the largest detectability is sought without violating regulatory requirements, is specific to each season, dosage, and choice of feedstock. OAE deployments should be tailored taking into account local hydrography, season, dosage, and feedstock characteristics. Our approach provides a practical avenue for optimizing deployments.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".