Socio-geographic and demographic analysis of the official national registry data of dogs’ population in Portugal in 2023. Data from SIAC
Bibliographic record
Abstract
National data on dog populations has been historically scarce, hindering effective policy and welfare efforts. The SIAC (Information System for Companion Animals) registry now provides a robust dataset for analyzing dog demographics in Portugal. This study examines SIAC data (2004–2023), analyzing breed, sex, reproductive status, age, and geographic distribution. Chi-square tests with post-hoc residual analysis identified significant demographic and breed variations across districts, while clustering based on standardized residuals grouped districts by distinct profiles. Geographic and socioeconomic distributions were assessed using the European Deprivation Index and urbanicity classification, with spatial visualizations generated in R and QGIS. The dataset includes 2,581,870 dogs, with a predominance of younger dogs (ages 2–3 years), a slight male majority (51.6 %), and 54.4 % neutered. Mixed-breed dogs were most common (39.6 %), followed by Portuguese Podengo (10.9 %) and Labrador Retriever (6.1 %). Age distributions varied geographically, with younger dogs in rural areas and older dogs in urban settings. Rural regions had higher dog-to-household ratios, while urban and economically deprived areas had higher absolute dog numbers but lower ratios. The findings establish baseline data for canine studies and highlight social patterns in the dog population. They underscore the role of rural areas in preserving native Portuguese breeds and the need for targeted public health and veterinary initiatives. By integrating demographic, geographic, and socioeconomic factors, this study provides key insights to inform public health and animal welfare policies, advocating for tailored strategies to meet the diverse needs of urban and rural canine populations. • Analysis of 2.58 M dogs in Portugal reveals demographic, breed, and geographic patterns. • Cluster analysis identifies similar age and breed patterns among neighboring municipalities. • Rural areas preserve native breeds, while urban centers favor smaller international breeds. • The proportion of dogs gradually increases with higher levels of socioeconomic deprivation.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".