Behaviour of the ocean DMS(P) pools in the Sargasso Sea viewed in a coupled physical-biogeochemical ocean model
Bibliographic record
Abstract
The dimethylsulfide (DMS) production model NODEM (Northern Oceans DMS Emission Model) was coupled with the water column ocean model GOTM (General Ocean Turbulence Model) that includes a two-equation kε turbulence scheme. This coupled physical-biogeochemical ocean model represents a significant improvement over the previous uncoupled version of NODEM that was driven by a diagnostic vertical mixing scheme. Using the same set of biogeochemical parameters, the coupled model is used to simulate the annual cycles of 1992 and 1993 at Hydrostation S in the Sargasso Sea. The better reproduction of the turbulent mixing environment corrects some deficiencies in nitrogen cycling, especially in the seasonal evolution of the nutrient concentrations. Hence, the coupled model captures the late-winter chlorophyll- and DMS(P)-rich blooms. It is also more adept at reproducing the vertical distribution of chlorophyll and DMS(P) in summer. Moreover, the DMS pool becomes less dependent on parameters controlling the nitrogen cycle and relatively more sensitive to parameters related to the sulfur cycle. Finally, the coupled model reproduces some of the observed differences in DMS(P) pools between 1992 and 1993, the latter being an independent data set not used in calibrating the initial version of NODEM.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".