MétaCan
Menu
Back to cohort
Record W2146946960 · doi:10.5539/ep.v1n1p20

The Implementation of Preventive Vaccination of Dogs and Cats against Rabies in Rural Areas

2011· article· en· W2146946960 on OpenAlexvenueno aff
Witold Kołłątaj, A. Milczak, Barbara Kołłątaj, Marian Sygit, Katarzyna Sygit

Bibliographic record

VenueEnvironment and Pollution · 2011
Typearticle
Languageen
FieldImmunology and Microbiology
TopicRabies epidemiology and control
Canadian institutionsnot available
Fundersnot available
KeywordsRabiesVaccinationRural areaVeterinary medicineMedicineCATSEnvironmental healthVirologyInternal medicinePathology

Abstract

fetched live from OpenAlex

Rabies is a fatal viral infection that has no specific treatment. For that reason prevention, especially vaccinations against rabies, is the matter of the utmost importance. The study involved 176 dog owners (possessing 257 dogs) and 86 cat owners (possessing 182 cats) from rural areas in Lublin province. The special original inquiry questionnaire was applied. Results: Preventive vaccination of dogs against rabies is properly realized by only 64.8 ± 7 % dog owners (below the level recommended by WHO) and by only 19.8 ± 9.1 % of cat owners - inhabitants of rural areas in Lublin Province. 16.5 % of respondents confessed that they have never vaccinated their dogs against rabies. 48.3 ± 7.4 % of dog owners as well as 65.1 ± 10.1 % of cat owners don't have any veterinary health certificates for their animals. Conclusions: The standards of veterinary care as well as effectiveness of dogs and cats vaccinations against rabies in rural areas in Poland need to be improved.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.008
GPT teacher head0.226
Teacher spread0.218 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations5
Published2011
Admission routes1
Has abstractyes

Explore more

Same venueEnvironment and PollutionSame topicRabies epidemiology and controlFrench-language works237,207