The effects of near-surface turbulence on CO2 flux at the ocean-atmosphere boundary 
Bibliographic record
Abstract
Ocean alkalinity enhancement (OAE) seeks to store carbon in the ocean as bicarbonate or carbonate ions and thus accelerates CO2 uptake from the atmosphere. Near-surface ocean turbulence is an important driver of CO2 uptake by the ocean as it affects the rate at which air-sea gas exchange occurs. Turbulent mixing can also cause high alkalinity water to sink out of the mixed layer, where it will no longer be in contact with the atmosphere. In this presentation we will show the results of high resolution numerical simulations in which alkalinity and dissolved inorganic carbon are advected in a turbulent mixed layer. By coupling the physics to a simple carbonate system solver, we evaluate the potential impact of surface turbulence on CO2 flux into the ocean. We explore the impact of ocean surface processes on the evolution and downwards diffusion of a surface alkalinity addition as influenced by different wind, temperature and precipitation conditions. The CO2 flux is computed according to both an empirical and a physically derived parameterization, and an estimate of the sensitivity of the total CO2 flux to the choice of parameterization is presented.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".