Deterministic and stochastic plankton dynamics: Effects of contamination, refuge, and additional food sources
Bibliographic record
Abstract
Studying plankton systems encompasses different interests, including understanding ecological cycles and developing sustainable strategies in aquaculture research regarding food security. Zooplankton farming is economically valuable, and its production may depend primarily on the availability of phytoplankton and other external food sources. However, diverse factors may affect overall phytoplankton–zooplankton interactions. For example, phytoplankton’s defense mechanisms, such as finding refuge and releasing toxins or low phytoplankton’s sustainable environments, can decrease zooplankton populations. Another critical factor is the adverse effects of pollution on plankton systems, which are more frequently present in water bodies. Still, zooplankton may survive harsh conditions if present pollutants are in low concentrations and external sources, including animal waste, are available. The partial understanding of these trophic interactions depends on initial assumptions, and using stochastic approaches may reduce the gap between deterministic mathematical outcomes and reality. In this work, we have mathematically described a planktonic system under the above assumptions using a deterministic model as well as its stochastic version. Our findings suggest that zooplankton growth is possible under polluted environments by providing them with external food sources, complementing phytoplankton availability. However, in these circumstances, random external environmental factors may cause the phytoplankton population to collapse. Through stochastic numerical experiments, we estimate which possible scenarios are more likely to induce phytoplankton extinction in these plankton systems.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| 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".