A zooplankton diel vertical migration parameterization for coastal marine ecosystem modeling
Bibliographic record
Abstract
Abstract. A simple parameterization of zooplankton vertical swimming is proposed as a way to reproduce the diel vertical migration (DVM) behavior, which refers to the daily descent of aquatic organisms hundreds of meters below the surface at dawn and their return to the surface at dusk, a phenomenon that is widespread among most zooplankton species. The swimming behavior is mechanistically parameterized as a function of the local irradiance and food availability, and is incorporated in a simple biogeochemical model coupled with a water column turbulence model in an Eulerian framework. The DVM behavior and its impact on plankton dynamics are investigated in an idealised configuration representing a marine coastal ecosystem. The sensitivity of the model to key parameters such as the zooplankton swimming speed, grazing rate, the optimal irradiance and turbulent diffusivity is evaluated with respect to three metrics representing the actual DVM behavior, the zooplankton-to-phytoplankton grazing coupling efficiency, and the vertical carbon export. Results show that the parameterization is able to reproduce the main characteristics of present knowledge about zooplankton DVM, and that the associated ecosystem responses are strongly sensitive to the maximum grazing rate, and moderately sensitive to other parameters.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".