The viability and safety of Ocean Alkalinity Enhancement: encouraging results from collaborative industry-academic field studies in Canada, the USA and the UK
Bibliographic record
Abstract
We have reached global scientific consensus regarding the urgent need for research, development, and early field testing of certain carbon dioxide removal (CDR) techniques. Ocean Alkalinity Enhancement (OAE) has emerged as one of the most promising CDR pathways, due in large part to its scalability, permanence, and potential for low-cost deployment. Accordingly, OAE research has accelerated dramatically in recent years, and Planetary Technologies, based in Nova Scotia, Canada, is widely recognized as a world leader in this space. In this presentation, we highlight the advancements made during 2+ years of safe and effective field trials, including: direct measurement of alkalinity enhancement near the deployment site, the lack of detectable impact to ecological systems, state-of-the-art ocean modeling systems, delivery of verified carbon removal credits via robust Measurement Reporting and Verification (MRV) techniques, dedicated and meaningful community engagement, and significant cost reductions with line of sight to economically viable OAE projects. Some consider open-system pathways like OAE to be challenged by detectability and complex MRV, but advancements by companies like Planetary, alongside findings from rapidly expanding peer-reviewed literature, suggest OAE could quickly become a viable and safe part of our climate solution portfolio.
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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 | 0.018 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.008 | 0.006 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| 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".