Subpolar gyre decadal variability explains the recent oxygenation in the Irminger Sea
Bibliographic record
Abstract
Abstract Accurate monitoring of the long-term trend of oxygen content at global scale requires a better knowledge of the regional oxygen variability at interannual to decadal time scale. Here, we combined the Argo dataset and repeated ship-based sections to investigate the drivers of the oxygen variability in the North Atlantic Ocean, a key region for the oxygen supply into the deep ocean. We focus on the Labrador Sea Water in the Irminger Sea over the period 1991–2018 and we show that the oxygen solubility explains less than a third of the oxygen variability. In turn, the main drivers of the oxygen variability are due to changes in vertical mixing, advection, and other processes as revealed by Apparent Oxygen Utilization computation. Our findings revealed the key role of physical processes on the changes in oxygen variability and highlight the need of keeping a sustained monitoring of those processes to disentangle human-induced changes in oxygen from decadal natural variability.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 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".