Geographic Variation in the Prevalence of <i>Candidatus</i> Neoehrlichia procyonis in Raccoons (<i>Procyon lotor</i>) in the United States and Canada
Bibliographic record
Abstract
Raccoons (Procyon lotor) are reservoirs for pathogens of other wildlife species, domestic animals, and humans, including several tick-borne pathogens. A relatively understudied organism in raccoons is Candidatus Neoehrlichia procyonis which has been detected in raccoons from the southeastern United States. A related species in Europe and Asia, Neoehrlichia mikurensis, uses rodents as reservoirs and Ixodes spp. as vectors; however, studies on rodents suggest they are not susceptible to Ca. N. procyonis. N. mikurensis has been associated with cases of neoehrlichiosis in people and dogs, which emphasizes the need to better understand the natural history of Ca. N. procyonis. We conducted a molecular survey of raccoons from selected regions of the United States and Canada. Of 394 raccoons tested, 167 (42.4%) were confirmed to be positive for Ca. N. procyonis based on sequence analysis. There was spatial variation in prevalence with significantly higher prevalence (68%, 268/394) being detected in the Southeast region of the United States compared with all other regions, although a high prevalence (55.1%, 217/394) was detected in California. Lower prevalence was detected in the Midwest (3.8%, 15/394) and none of the raccoons from Canada were positive. These data suggest that Ca. N. procyonis is widespread in raccoon populations in the United States but there is spatial variation which may be related to vector distribution or some other factor. Although not known to infect hosts other than raccoons, neoehrlichiosis should be considered in cases of suspected ehrlichiosis in immunocompromised dogs or people that have no known etiologic agent.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".