Vector-borne and other pathogens of potential relevance disseminated by relocated cats
Bibliographic record
Abstract
Large populations of unowned cats constitute an animal welfare, ecological, societal and public health issue worldwide. Their relocation and homing are currently carried out in many parts of the world with the intention of relieving suffering and social problems, while contributing to ethical and humane population control in these cat populations. An understanding of an individual cat's lifestyle and disease status by veterinary team professionals and those working with cat charities can help to prevent severe cat stress and the spread of feline pathogens, especially vector-borne pathogens, which can be overlooked in cats. In this article, we discuss the issue of relocation and homing of unowned cats from a global perspective. We also review zoonotic and non-zoonotic infectious agents of cats and give a list of practical recommendations for veterinary team professionals dealing with homing cats. Finally, we present a consensus statement consolidated at the 15th Symposium of the Companion Vector-Borne Diseases (CVBD) World Forum in 2020, ultimately to help veterinary team professionals understand the problem and the role they have in helping to prevent and manage vector-borne and other pathogens in relocated cats.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".