Assessing the potential for dimethylsulfide enrichment at the sea surface and itsinfluence on air–sea flux
Bibliographic record
Abstract
Abstract. The flux of dimethylsulfide (DMS) to the atmosphere is generally inferred using water sampled at or below 2 m depth, thereby excluding any concentration anomalies at the air–sea interface. Two independent techniques were used to assess the potential for near-surface DMS enrichment to influence DMS emissions and also identify the factors influencing enrichment. DMS measurements in productive frontal waters over the Chatham Rise, east of New Zealand, did not identify any significant gradients between 0.01 and 6 m in sub-surface seawater, whereas DMS enrichment in the sea-surface microlayer was variable, with a mean enrichment factor (EF; the concentration ratio between DMS in the sea-surface microlayer and in sub-surface water) of 1.7. Physical and biological factors influenced sea-surface microlayer DMS concentration, with high enrichment (EF > 1.3) only recorded in a dinoflagellate-dominated bloom, and associated with low to medium wind speeds and near-surface temperature gradients. On occasion, high DMS enrichment preceded periods when the air–sea DMS flux, measured by eddy covariance, exceeded the flux calculated using National Oceanic and Atmospheric Administration (NOAA) Coupled-Ocean Atmospheric Response Experiment (COARE) parameterized gas transfer velocities and measured sub-surface seawater DMS concentrations. The results of these two independent approaches suggest that air–sea emissions may be influenced by near-surface DMS production under certain conditions, and highlight the need for further study to constrain the magnitude and mechanisms of DMS production in the sea-surface microlayer.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".