Bibliographic record
Abstract
Predators can not only control population size of prey through direct killing,but also influence reproductive outputs and population dynamics of prey through predation risk effects,which are the costs incurred by prey's anti-predator behavioral change.Predation risk effects can have even stronger influences on prey population dynamics than direct killing of prey,but there have been relatively smaller number of field studies about the impacts of predation risk effects on population dynamics of preys during the past decades.This paper reviews the progress of researches about predation risk effects on population dynamics of preys with emphasis on introduction of several classical case studies involving Wolf(Canis lupus)-Elk(Cervus elephus) system in Yellowstone National Park of USA and Predator-snowshoe Hare(Lepus americanus) system in Yukon of Canada.This paper elaborates two hypotheses(predator-sensitive-food hypothesis and predation stress hypothesis) that have been proposed to illustrate the mechanisms underlying the impacts of predation risk on nutrition,reproduction and population dynamics of preys.There is evidence to support both hypotheses,but more researches are needed to further verify them.Considering that there have been few field studies about predation risk effects on prey population dynamics in China and the fact that many endangered species are facing increasing natural and anthropogenic stresses in China,I put forward recommendations and urge to conduct researches on predation risk effects(in addition to direct predation effects) on population dynamics of endangered ungulate preys in China.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".