First report of the deer ked, <i>Lipoptena cervi</i>, and associated pathogens in southern Québec, Canada
Bibliographic record
Abstract
Deer keds (Lipoptena cervi), an introduced European species, are expanding their geographic range in North America. We document their first recorded presence in Québec, Canada, map their distribution, and highlight the detection of pathogens of potential public health relevance. In the Estrie region of southern Québec, 47 deer keds (L. cervi) were collected from 14 (5.5%) of 254 harvested white-tailed deer (Odocoileus virginianus). Borrelia spp. and Anaplasma phagocytophilum were detected in the body of 1/44 and 8/44 L. cervi specimens, respectively. A statistically significant spatial cluster of white-tailed deer infested by L. cervi was found in southern Estrie using the Bernoulli-based spatial scan statistic.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 teacher head, 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".