Survival analysis and density function of stochastic stage-structured cannibalism dynamics
Bibliographic record
Abstract
Cannibalism and external environmental disturbances significantly impact the survival of biological populations. To analyze their combined effects comprehensively, we introduce a novel stochastic predator-prey model incorporating stage structure and cannibalism in predators. We investigate the existence, uniqueness, and ultimate boundedness of positive solutions for the model, and conduct survival analysis in the presence or absence of cannibalism. Additionally, we derive an approximate expression for the explicit density function of the ergodic stationary distribution. In contrast to the scenario without cannibalism, the introduction of cannibalism leads to the following outcomes: (1) In the absence of environmental noise, mature predators exhibit higher population sizes, while juvenile predators and prey show lower population sizes; (2) Mild environmental noise makes both prey and mature predators more vulnerable, while juvenile predators display stronger resistance; (3) Intense environmental interference for predators results in predator population extinction, even if prey experience low levels or no environmental noise; (4) Extinction occurs across all populations when prey faces significant environmental noise. Thus, under inevitable environmental disturbances, cannibalism serves as an adaptive mechanism, enhancing the survival of mature predators and bolstering their resilience to external interference. This reduces the risk of extinction and promotes biodiversity maintenance.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".