Ocean Alk-Align: an international research project to assess the potential of Ocean Alkalinity Enhancement for marine Carbon Dioxide Removal
Bibliographic record
Abstract
Of the various marine Carbon Dioxide Removal (CDR) technologies proposed to date, ocean alkalinity enhancement (OAE) has, arguably, the largest carbon removal potential. OAE has several advantages over other approaches: it does not compete for nutrient use, it is applicable to large regions of the coastal and open ocean, it can mitigate ocean acidification, and it has a high potential for permanence. Consequently, a growing number of private-sector innovators are actively pursuing OAE, leading to the potential risk that independent, non-profit-oriented research will fall behind in providing a balanced assessment of OAE.The Ocean Alk-Align project is a multi-year research effort involving an international consortium of researchers from Canada, Germany, and Australia. The project seeks to increase knowledge on three key research topics essential for OAE implementation: (1) efficiency and durability of CO2 removal; (2) environmental safety; (3) monitoring and verification. This will be done through the development and application of state-of-the-art experimental research, real-world observations, and near-field to Earth system modeling.The Ocean Alk-Align project will use a multi-scale combination of laboratory and field experimentation in addition to turbulent-, regional-, and large-scale modelling. This presentation will provide an overview of ongoing and planned activities as well as some early results.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 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 | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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 teacher head, 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".