Statistics of surface divergence and their relation to air‐water gas transfer velocity
Bibliographic record
Abstract
Air‐sea gas fluxes are generally defined in terms of the air/water concentration difference of the gas and the gas transfer velocity, k L . Because it is difficult to measure k L in the ocean, it is often parameterized using more easily measured physical properties. Surface divergence theory suggests that infrared (IR) images of the water surface, which contain information concerning the movement of water very near the air‐water interface, might be used to estimate k L . Therefore, a series of experiments testing whether IR imagery could provide a convenient means for estimating the surface divergence applicable to air‐sea exchange were conducted in a synthetic jet array tank embedded in a wind tunnel. Gas transfer velocities were measured as a function of wind stress and mechanically generated turbulence; laser‐induced fluorescence was used to measure the concentration of carbon dioxide in the top 300 μ m of the water surface; IR imagery was used to measure the spatial and temporal distribution of the aqueous skin temperature; and particle image velocimetry was used to measure turbulence at a depth of 1 cm below the air‐water interface. It is shown that an estimate of the surface divergence for both wind‐shear driven turbulence and mechanically generated turbulence can be derived from the surface skin temperature. The estimates derived from the IR images are compared to velocity field divergences measured by the PIV and to independent estimates of the divergence made using the laser‐induced fluorescence data. Divergence is shown to scale with k L values measured using gaseous tracers as predicted by conceptual models for both wind‐driven and mechanically generated turbulence.
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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 | 0.001 | 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 | 0.000 | 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 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".