ECO-EPIZOOTIOLOGIC STUDY OF FRANCISELLA TULARENSIS, THE AGENT OF TULAREMIA, IN QUÉBEC WILDLIFE
Bibliographic record
Abstract
In Canada, Francisella tularensis , the zoonotic bacterial agent of tularemia, affects mostly snowshoe hares ( Lepus americanus ), muskrats ( Ondatra zibethicus ), and beavers ( Castor canadensis ). Despite numerous studies, the ecologic cycle and natural reservoirs of F. tularensis are not clearly defined. We conducted a cross-sectional study to estimate the prevalence of F. tularensis in snowshoe hares, muskrats, and coyotes ( Canis latrans ) in four regions of Québec, Canada, and to describe the risk of infection in relation to host and environmental characteristics at three spatial scales. Between October 2012 and April 2013, trappers captured 345 snowshoe hares, 411 muskrats, and 385 coyotes. Blood samples were tested by microagglutination tests, and DNA extracts of liver, kidney, lung, and spleen of snowshoe hares and muskrats were tested by real-time PCR to detect past and active infection to F. tularensis , respectively. Individual host characteristics, including body condition, age, and sex, were evaluated as risk factors of infection, along with ecologic characteristics of the location of capture extracted from geographic databases. Prevalences of antibody to F. tularensis and 95% confidence intervals were 2.9% (1.4-5.1%) in coyotes, 0.6% (0.1-2.1%) in hares, and 0% (0.0-0.9%) in muskrats. Francisella tularensis DNA was not detected by real-time PCR in the pools of four organs from muskrats and hares, but F. tularensis type AI was detected during testing of the individual organs of two antibody-positive hares. Exact logistic regression analyses showed that age was a significant predictor of antibody detection in coyotes, as were the proportion of forest and the proportion of area considered as suitable habitat for hares in the environment around the location of capture of the coyotes. Our results suggest a terrestrial cycle of F. tularensis in the regions studied.
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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 | 0.001 |
| 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 | 0.000 | 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".