Diagnóstico de la población canina y felina en los hogares de las parroquias urbanas del cantón Cuenca, provincia del Azuay
Bibliographic record
Abstract
La presente investigación tiene como objetivo general diagnosticar la población canina y felina en las 15 parroquias urbanas del cantón Cuenca, tomando en cuenta las siguientes variables: sexo, raza, edad, estado reproductivo, manejo sanitario y tipo de alimentación, por lo cual se realizaron 1307 encuestas a hogares tomados al azar. El 60.04%(caninos) y 58.25%(felinos) son machos, la edad que predomina en caninos y felinos es de 12 a 60 meses con 59.62% y 53.61% respectivamente. \nHan sido vacunados contra la rabia el 79.49% de los caninos y el 51.55% de los felinos, contra otras enfermedades el 52.35% de los caninos y el 33.51% de los felinos, desparasitación interna de caninos y felinos: 75.33% y 61.34% respectivamente. Las razas que predominan en caninos son: Mestizos, Poodle, Schnauzer, Bulldog, Shih tzu, Pequinés, Golden Retriever, Labrador, Pug, Chihuahua, Beagle, Basset Hound, Dash Hound, Pastor Alemán, Husky Siberiano, Pitt Bull y Sharpei, y en felinos: Doméstico, Himalayo, Persa y Siamés. \nSe demostró que la mayoría de caninos y felinos no están esterilizados constituyendo el 81.82% y 76.81% respectivamente. La alimentación de caninos y felinos es de una dieta mixta, 44.18% y 36.86% respectivamente. \nLa relación encontrada de caninos: hogar es de 1,5:1 y felinos: hogar 0,3:1
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".