Climate-sensitive zoonotic diseases transmissible by companion animals: A scoping review protocol
Bibliographic record
Abstract
OBJECTIVE: This joint protocol describes two scoping reviews that will identify and describe evidence for climate sensitivity of companion animal zoonotic diseases in cat, dog, and human populations worldwide. INTRODUCTION: Climate change is a driver for emerging and re-emerging zoonotic diseases of global health concern. Companion animals can transmit over 70 zoonotic pathogens, some of which are sensitive to changes in meteorological factors. There is disparate evidence in our understanding of climate-sensitive companion animal zoonotic diseases. INCLUSION CRITERIA: Primary research articles that describe 1) an association or effect between meteorological factors and the risk of zoonotic disease, 2) the presence of spatiotemporal variations in disease incidence or prevalence, or 3) the projected impacts of climate emission scenarios on disease trends will be included. METHODS: A comprehensive search strategy was developed using index terms and keywords for populations of interest, companion animal zoonotic diseases, and meteorological factors. Articles will be searched on MEDLINE (via Ovid), AGRICOLA (via ProQuest), and Web of Science. Additional articles will be identified using citation tracking. Independent reviewers will systematically apply a two-step study screening process based on defined eligibility criteria. Key study characteristics and findings will be collated and presented as a descriptive summary using graphical and tabular formats. REVIEW REGISTRATION: Two separate protocols have been registered in Open Science Framework. The first review consolidates evidence in cat and dog populations (https://osf.io/ydgc2), while the second review is focused on human populations (https://osf.io/3cvx2).
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 | 0.122 | 0.119 |
| Meta-epidemiology (narrow) | 0.004 | 0.004 |
| Meta-epidemiology (broad) | 0.010 | 0.011 |
| Bibliometrics | 0.019 | 0.013 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.006 | 0.009 |
| Research integrity | 0.010 | 0.006 |
| Insufficient payload (model declined to judge) | 0.092 | 0.028 |
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".