Dynamics of a Fractional-Order Prey-Predator Model with Fear Effect and Harvesting
Bibliographic record
Abstract
This study examines a fractional-order prey-predator model incorporating fear effects and harvesting impacts on prey dynamics, employing both continuous and discretized frameworks with the Monod-Haldane functional response.The existence, uniqueness, and boundedness of the system's solutions, along with their non-negativity, are established through rigorous analysis.The system is further evaluated for potential equilibrium points, with their stability conditions meticulously assessed.It is revealed that the model possesses three locally stable equilibrium points, provided certain conditions are met.In the context of the discretized model, an optimal harvesting strategy is formulated, guided by Pontryagin's Maximum Principle, to ensure maximum economic yield.Numerical simulations complement the analytical findings, offering insights into the system's dynamic behavior under both continuous and discrete scenarios.Moreover, the optimality problem associated with harvesting strategies is resolved.The study concludes by summarizing the significant outcomes and their implications for ecological management.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 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".