Evaluation of bait flavors for potential use in oral rabies vaccine delivery to feral dogs (Canis familiaris)
Bibliographic record
Abstract
It is estimated that less than 20% of domestic dogs on tribal lands in the United States are vaccinated against rabies. One potential method to increase vaccination rates may be the distribution of oral rabies vaccines (ORV). ONRAB® is the primary ORV bait used in Canada to vaccinate striped skunks and raccoons. Research has suggested the most common non-target animals that may ingest these baits are feral domestic dogs. To further investigate the potential use of ONRAB® ORV baits to vaccinate feral domestic dogs against rabies on tribal lands, we performed a flavor preference study to increase ORV acceptance. Seven bait flavors (bacon, cheese, dog food, hazelnut, marshmallow, peanut butter and sardine) were offered in pairs to 13 domestic dogs. Each dog was offered all possible combinations of bait pairs over a period of ten days, with each bait offered six times. The proportion of times each bait was consumed first by individual dogs was calculated and comparisons among dogs were conducted. Dog food was selected first 56% of the time, and more frequently than all other bait types (F = 13.09, P = 0.0005) although bacon was close second at 54%. Marshmallow was selected first during 14% of offerings and exhibited the least preference among all bait types (F = 22.46, P < 0.0001). A more extensive evaluation is planned, preliminarily; dog food or bacon flavored ORV baits appear to be good choices for optimizing bait ingestion by feral domestic dogs.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".