Is Carriage of Leptospira interrogans by Rats Influenced by the Urban Environment or Population Density?
Bibliographic record
Abstract
Leptospira interrogans is one of the most important zoonotic pathogens globally. In urban settings, Norway rats (Rattus norvegicus) are important reservoirs of L. interrogans, but it is unclear how this bacterium is transmitted among rats. Both environmental features and rat population density may determine the prevalence of this pathogen in rat populations as well as the spillover risk to people. While these factors could play an important role in transmission between rats, it is unknown whether such factors influence prevalence among rats at a fine scale. Our objective was to determine if carriage of L. interrogans by rats could be explained by variation in the environment or in rat population density. Rats were live-trapped in a single neighborhood of Vancouver, Canada during two study periods (2011-12; 2016-17) and were tested for L. interrogans. The physical environment of each city block was recorded using a comprehensive, in-person environmental survey. Using generalized linear mixed modelling, we found no evidence of an association between carriage of L. interrogans and environmental features or rat population density, suggesting that these were not the primary drivers of its distribution among rats within this neighborhood. Understanding factors that promote L. interrogans transmission can be used to inform management approaches to minimize public health risks.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".