Bibliographic record
Abstract
Three basic forms of management strategies exist for wildlife disease, as follows: prevention of introduction of disease, control of existing disease or eradication. Management may be directed at the disease agent, host population, habitat or be focused on human activities. Disease agents may be dealt with in the environment through disinfection or in the host through treatment. Disinfection and pesticides used to destroy agents or vectors are limited to local situations, may have serious environmental effects and may result in acquired resistance. Difficulty in delivering treatment limits chemotherapy to local situations. Host populations may be managed by immunisation, by altering their distribution or density, or by extirpation. Immunisation is best suited for microparasitic exogenous infections with a low reproductive rate and in populations which have a low turnover. Mass immunisation with oral baits has been effective, but this strategy is limited to a few serious diseases. It is difficult to move wild animals and techniques to discourage animals from entering an area become ineffective rapidly. The setting up of fences is feasible only in local situations. Selective culling is limited to situations in which affected individuals are readily identifiable. General population reduction has had little success in disease control but reducing populations surrounding a focus or creating a barrier to disease movement have been successful. Population reduction is a temporary measure. Eradication of a wildlife population has not been attempted for disease management. Habitat modification may be used to reduce exposure to disease agents, or to alter host distribution or density. Management of diseases of wild animals usually requires a change in human activities. The most important method is by restricting translocation of wild animals to prevent movement of disease.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.022 | 0.004 |
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".