Upper ocean bubble measurements from the NE Pacific and estimates of their role in air‐sea gas transfer of the weakly soluble gases nitrogen and oxygen
Bibliographic record
Abstract
Simultaneous observations of upper‐ocean bubble clouds, and dissolved gaseous nitrogen (N2) and oxygen (O2) from three winter storms are presented and analyzed. The data were collected on the Canadian Surface Ocean Lower Atmosphere Study (C‐SOLAS) mooring located near Ocean Station Papa (OSP) at 50°N, 145°W in the NE Pacific during winter of 2003/2004. The bubble field was measured using an upward looking 200 kHz echosounder. Direct estimates of bubble mediated gas fluxes were made using assumed bubble size spectra and the upward looking echosounder data. A one‐dimensional biogeochemical model was used to help compare data and various existing models of bubble mediated air‐sea gas exchange. The direct bubble flux calculations show an approximate quadratic/cubic dependence on mean bubble penetration depth. After scaling from N2/O2 to carbon dioxide, near surface, nonsupersaturating, air‐sea transfer rates, KT, for U10 > 12 m s−1 fall between quadratic and cubic relationships. Estimates of the subsurface bubble induced air injection flux, VT, show an approximate quadratic/cubic dependence on mean bubble penetration depth. Both KT and VT are much higher than those measured during Hurricane Frances over the wind speed range 12 < U10 < 23 m s−1. This result implies that over the open ocean and this wind speed range, older and more developed seas which occur during winter storms are more effective in exchanging gases between the atmosphere and ocean than younger less developed seas which occur during the rapid passage of a hurricane.
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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 | 0.000 | 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 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".