THE ROLE OF BIOCONVECTION IN PLANKTON POPULATION WITH THERMAL STRATIFICATION
Bibliographic record
Abstract
This paper investigates hydrodynamic processes of suspended planktonic population within a thermal stratification to represent a thermocline, in using a model of coupled equations: Navier–Stokes' equation for the fluid dynamic motion, Fisher's equation for the planktonic population with logistic growth, energy conservation equation for thermal effects and the Boussinesq approximation for the dependence on mass density of plankton. The planktonic population is assumed to be slightly denser than the fluid and to follow logistic growth. A numerical investigation is conducted to study patterns of plankton distribution under thermal stratification, and we adopt the finite volume method for solving the system of governing equations in simulations. It is found that thermal stratification constrains the feedback between hydrodynamic and plankton dynamics. More precisely, when temperature is lower in the upper than in the lower layer, the temperature gradient is destabilizing and both convection mechanisms — plankton motility (i.e. taxis behavior that can induce bioconvection) and temperature gradient — are additive. In the case of higher temperature in the upper than the lower layer, which applies to most natural systems, thermal stratification can dampen bioconvection and induce oscillatory dynamics if plankton diffuses faster than temperature. In all cases, if hydrodynamic and demographic time scales overlap, plankton growth rate has a strong effect on hydrodynamic processes involving both thermal convection and convection created by gravitaxis mechanism. Our study thus reveals the potential feedback between hydrodynamic and local demographic processes in plankton populations.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".