Air‐sea gas transfer velocity for oxygen derived from float data
Bibliographic record
Abstract
We estimated the air‐sea gas transfer velocity for oxygen using 3 consecutive years (September 2003 to August 2006) of high‐quality oxygen measurements from profiling floats in the central Labrador Sea. Mixed layer oxygen concentrations exhibit strong seasonality characterized by biologically and thermally driven evasion during spring/summer and invasion during fall/winter caused by cooling and ventilation of oxygen deficient subsurface waters. Mixed layer oxygen budgets entirely excluding the spring bloom period are employed to estimate the air‐sea transfer velocity for oxygen. By using colocated wind speed data acquired by scatterometry from the QuikSCAT satellite, wind speed‐dependent parameterizations for the air‐sea gas transfer velocity k660 (CO2 at 20°C and salinity 35) are established and compared with prominent parameterizations from the literature. Quadratic, cubic, and quartic functions are fitted to the data for short‐term and long‐term wind speed averages separately. In both cases, the quadratic functions yield the poorest fit to the observations. Overall, the stronger curvature of the cubic functions provides the best fit, while the quartic function also fits the data less well. Our results generally confirm the stronger wind speed dependencies among the suite of published parameterizations. Also, the better fits found for cubic function point at the strong importance of very high wind speed for air‐sea gas exchange of O2.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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) | 0.000 | 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".