Assessment of a global climatology of oceanic dimethylsulfide (DMS) concentrations based on SeaWiFS imagery (1998-2001)
Bibliographic record
Abstract
A method is developed to estimate sea-surface particulate dimethylsulfoniopropionate (DMSPp) and dimethylsulfide (DMS) concentrations from sea-surface concentrations of chlorophyll a (Chl a). When compared with previous studies, the 1° × 1° global climatology of oceanic DMS concentrations computed from 4 years (19982001) of Chl a measurements derived from SeaWiFS (satellite-based, sea-viewing wide field of view sensor) exhibits lower seasonal variability in the southern hemisphere than in the northern hemisphere. A first evaluation of the method shows that it reasonably well represents DMSPp and DMS in the North Atlantic subtropical gyre, in large blooms of mixed populations of diatoms and Phaeocystis spp., and in massive blooms of Phaeocystis spp. but fails for large, almost pure blooms of diatoms. DMSPp and DMS concentrations derived from SeaWiFS were also compared with spatially and temporally coincident in situ measurements acquired independently in the Atlantic between 39°N and 45°N and in subtropical and subantarctic Indian Ocean surface waters. Moderate spring and summer phytoplankton blooms there exhibited similar trends in DMSPp and DMS levels vs. moderate blooms of mixed populations of prymnesiophytes and dinoflagellates investigated by others. Measured DMS largely exceeded simulated DMS concentrations, whereas measured and simulated DMSPp levels were in close agreement. DMS accumulation is tentatively attributed to dinoflagellate DMSP lyase activity.
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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) | 0.001 | 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".