Near surface bubble, gas and flow measurements during the Bubble Exchange in the Labrador Sea (BELS) cruise – early results
Bibliographic record
Abstract
The bubbles formed by breaking waves are thought to play an important role in increasing gas transfer across the atmosphere-ocean surface during high wind conditions (>15 m/s). However, real world data on near-surface bubbles with sufficient resolution in space, time and bubble size to understand exactly how the transfer mechanisms work is rare. In addition, there are almost no data showing the relationship between bubble size distributions and the local flow and gas saturation conditions, although data from the HiWinGS cruise suggests that these structures could be very important for gas transfer. The BELS project data was collected during five weeks in November/December 2023, and includes tracer-based gas flux measurements, physical oceanography, and ocean chemistry. Hourly averaged wind speeds were 5-30 m/s, with maximum significant wave height of 11 m. Here, we will present early results from the part of the project monitoring near-surface bubbles and their relationship to flow patterns and dissolved gas concentrations in the top five metres of the ocean. Data will be presented from a free-floating buoy carrying specialised bubble cameras at 1m and 3m, ADCPs and oxygen optodes. We will show measured bubble size distributions, and the spatial relationship of these bubbles to Langmuir circulation patterns and dissolved oxygen concentrations. We will also present an early analysis of the relationships between gas carried by both the water itself and the bubbles, and how this relates to the advection of these two gas reservoirs in the top few metres of the ocean.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".