A deep-learning estimate of the decadal trends in the Southern Ocean carbon storage
Bibliographic record
Abstract
Uptake of atmospheric carbon by the ocean, especially at high latitudes, plays an important role in offsettinganthropogenic emissions. At the surface of the Southern Ocean south of 30◦S, the ocean carbon uptake, whichhad been weakening in 1990s, strengthened in the 2000s. However, sparseness of in-situ measurements in theocean interior make it difficult to compute changes in carbon storage below the surface. Here we develop amachine-learning model, which can estimate concentrations of dissolved inorganic carbon (DIC) in the SouthernOcean up to 4 km depth only using data available at the ocean surface. Our model is fast and computationally inexpensive. We apply it to calculate trends in DIC concentrations over the past three decades and find that DIC decreased in the 1990s and 2000s, but has increased, in particular in the upper ocean since the 2010s.However, the particular circulation dynamics that drove these changes may have differed across zonal sectorsof the Southern Ocean. While the near-surface decrease in DIC concentrations would enhance atmospheric CO2 uptake continuing the previously-found trends, weakened connectivity between surface and deep layersand build-up of DIC in deep waters could reduce the ocean’s carbon storage potential.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".