Prevalence of canine monocytic ehrlichiosis in dogs in and around Meerut district of Uttar Pradesh, India
Bibliographic record
Abstract
The present study was conducted to explore the prevalence of canine monocytic ehrlichiosis (CME) in dogs in and around Meerut district for its better clinical management. A total of 366 dog’s blood samples were collected from Department of Veterinary Clinical Complex, SVPUAT, Meerut and nearby private Veterinary Clinics with the history of tick infestation and characteristic clinical findings with CME and were screened on the basis of blood smear examination, followed by molecular detection by polymerase chain reaction during the period from January 2022 to June 2022. The blood smear examination with Giemsa stain detected morulae of E. canis and it showed as intracytoplasmic inclusion bodies of varying sizes and shapes in monocytes. Thirteen dogs were found positive for canine ehrlichiosis resulting in a prevalence of 3.55%. The highest affection of E. canis was found within the age group of 1-3 years (38.4%), followed by the 4-5 years age group (30.76%), then 6-7 year of age group (23.07%) and lowest infection levels (7.69%) were found in the age group of 4 month -1 year. The maximum prevalence of canine ehrlichiosis was found in Labrador (6.15%) followed by Rottweiler (5.55%), Bull mastiff (4.16%), Golden retriever (3.22%), German shepherd (2.5%), Pitbull (2.40%), and non-descriptive (1.40%) breed of dog. Higher prevalence was recorded in males (4.10%) in comparison to females (2.92%).
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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 | 0.000 | 0.000 |
| 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 | 0.000 | 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".