Population Estimation and Demographic Characteristics of Free-Roaming Dogs on Kasetsart University Kamphaeng Saen Campus, Thailand: Implications for Rabies Control
Bibliographic record
Abstract
Free-roaming dogs (FRDs) pose significant public health and animal welfare challenges globally, particularly in developing nations, where they serve as major reservoirs for zoonotic diseases, especially rabies. Determining the population size of FRDs, their demographic characteristics, and the factors influencing population size is essential for effective dog population management, which constitutes a critical component of rabies control programs. This study aimed to estimate the size and describe the characteristics of the FRD population and evaluate temporal and spatial variations in FRD sightings on the Kasetsart University Kamphaeng Saen Campus, Thailand. Factors influencing population size were assessed using a negative binomial regression model. The campus area was divided into 52 accessible blocks for dog-counting purposes. In 2018 and 2019, each block was surveyed once annually for a comprehensive census. In 2021, each block was surveyed on six occasions, three in the morning and three in the evening, using a photographic recapture method. Dogs sighted were photographed, and their characteristics and spatial coordinates were recorded. The total population sizes of FRDs on campus were documented as 529, 577, and 531 in 2018, 2019, and 2021, respectively. Intact female dogs predominantly constituted the majority of the FRD population. Regression analysis indicated a significant association between the number of FRDs within a block and the number of intact adult female dogs. The percentage of dogs sighted per occasion was significantly higher during evening surveys compared to morning surveys. This study proposes a practical census approach that combines total block counts with photographic recapture to estimate the population of FRDs. The findings offer valuable insights for developing effective dog population management strategies and planning rabies vaccination campaigns.
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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 | 0.000 | 0.001 |
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".