A System Dynamics Model for Assessing the Efficacy of Lethal Control for Sustainable Management of Ochotona curzoniae on Tibetan Plateau
Bibliographic record
Abstract
When population abundances exceed the economical or ecological threshold, animals are always regarded as pests, so effective and sustainable management strategies are required. As a native species widely distributed on Tibetan Plateau, plateau pika is regarded as a pest when its abundance is too high. For decades, plateau pika were controlled using toxic baits by both the local government and shepherds. However, how pika population fluctuates after lethal control is far from certain. Based on our previous studies, demographic parameters of plateau pika were acquired. A system dynamics model is developed to parameterize a population model for this species. The model incorporates two age categories (juvenile and adult) of both sexes, and uses density-dependent factors, including reproduction, mortality, and migration. The model is used originally to analyze the effect of pika management on the population dynamics and resulting abundance, in order to plan optimal pest controlling strategies. The results show that lethal control is efficient when continuously conducted once each year, or twice with 2-year intervals. For sustainable controlling pest abundance, comprehensive strategies should be considered. An appropriate population model could be used to explore the optimal strategies and provide important reference into decision making about pest 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 | 0.001 | 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".