Bibliographic record
Abstract
The effect of predation on population dynamics of black-tailed and mule deer (i.e., deer) can vary widely, depending on several factors specific to each local circumstance and time period including the amount of forage available for deer, the predator community, weather, disease, and human development. This chapter will provide managers with expected predation rates specific to ecoregion, as a starting point for designing a deer monitoring or management plan. The best documented effects of predation on deer include high black bear predation on neonates in California, mountain lion predation accelerating population decline caused by harsh weather and slowing population recovery in California, mountain lion predation affecting mule deer through apparent competition with white-tailed deer in the northwestern United States and southwestern Canada, and wolf predation causing declines in Sitka black-tailed deer populations on islands in southeast Alaska. Adjusting harvest regulations for mule deer to reach management objectives while accounting for predation can result in dissatisfaction among some stakeholders when predation rates are high and hunting opportunity is limited, as can efforts at carnivore control designed to increase hunter opportunity in such situations. Predation effects on deer populations are complex and the strong public interest requires monitoring programs and public transparency.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.050 | 0.007 |
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".