Effect of discontinuous harvesting on a diffusive predator-prey model
Bibliographic record
Abstract
Abstract The management of predator-prey systems, particularly those with discontinuous harvesting, plays a crucial role in maintaining ecological balance and ensuring the sustainable use of renewable resources. Despite the importance of this topic, the dynamics of diffusive predator-prey models with discontinuous harvesting have not been thoroughly explored in existing literature. This study addresses this gap by investigating a diffusive predator–prey model incorporating a discontinuous harvesting function. We establish the existence and boundedness of solutions, analyse the conditions under which a positive steady state is achieved, and explore the model’s stability, including global asymptotic stability and convergence in finite time. Additionally, we examine the effects of Turing instability, Hopf bifurcation, and steady-state bifurcation within the model. Numerical simulations are provided to illustrate the impact of discontinuous harvesting on the system’s dynamics, highlighting the practical applications of the theoretical results in fields such as pest control. The findings of this study offer valuable insights for the design of effective population management strategies in ecological and agricultural contexts.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".