Turbulent mixing and phytoplankton life history: a Lagrangian versus Eulerian model comparison
Bibliographic record
Abstract
Phytoplankton dynamics models follow either an Eulerian or a Lagrangian approach. The Eulerian formulation assumes that all individuals of a population living in homogeneous environmental conditions, i.e. within a model grid cell, are in a single average physiological state, which generally depends on local conditions. By tracking each individual cell or cluster of cells, the Lagrangian formulation allows population behaviour to emerge from a broader range of individual physiological states inherited from different life histories. In order to determine in which mixing conditions the widely used Eulerian approach differs from a more representative but also more computationally costly Lagrangian formulation, we compared the results obtained from a simple 1-dimensional phytoplankton growth model using both formulations under various mixing conditions. The chosen model is based on Droop kinetics, where growth is a function of light and an internal nutrient cell quota. It is applied in cases with constant and uniform diffusivity, and in more realistic cases of wind-induced and tidal mixing. The 2 main outcomes of our study are: (1) results from both formulations converge in weakly stratified environments for any level of turbulent mixing, and (2) results diverge in stratified environments and intermediate mixing up to a diffusivity value above which the environment appears homogeneous to moving cells, and both formulations converge. These results suggest that in heterogeneous and dynamic marine environments, strong variability among individuals may prevent Eulerian models from accurately predicting phytoplankton production.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".