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Record W2167398901 · doi:10.6000/jbs.v1i1.71

Prevalence of Parasitic Infections in Buffaloes in and around Ludhiana District, Punjab, India: A Preliminary Study

2012· article· en· W2167398901 on OpenAlexvenueno aff
Nirbhay Singh, Harkirat Singh, Jyoti Jyoti, Manjurul Haque, S. S. Rath

Bibliographic record

VenueJournal of Buffalo Science · 2012
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicVector-Borne Animal Diseases
Canadian institutionsnot available
FundersGuru Angad Dev Veterinary and Animal Sciences University
KeywordsVeterinary medicineSocioeconomicsGeographyBiologyMedicineSociology

Abstract

fetched live from OpenAlex

A total of 598 buffaloes were sampled for both coprological (210) and haematological (388) investigations at the Large Animal Clinics, GADVASU, Ludhiana, Punjab, India. Coprological examination revealed that the overall prevalence of gastrointestinal (GI) parasitic infections was 23.33% (49/210). Among the revealed parasites, amphistomes, Fasciola spp., Eimeria spp., Balantidium coli and strongyles were in 4.29, 3.33, 0.95, 2.86 and 15.71% of the examined buffaloes. Except coccidiosis, there was no significant variation of GI infections in relation to sex. Eimeria spp. was significantly higher in males. The present work emphasized that strongyles were the most prevalent gastrointestinal parasites found during coprological examination of buffaloes in Punjab, India. Examination of Giemsa-stained peripheral blood smears exhibited that 4.9% (19/388) of buffaloes were infected with haemoparasites comprising Theileria annulata (2.32%), Trypanosoma evansi (1.8%), Babesia bigemina (0.26%) and Anaplasma marginale (0.77%). Mixed infection appeared in one (0.26%) animal. Trypanosomosis was predominant in elder animals with no infection recorded in males.

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)0.000
metaresearch head score (Gemma)0.001
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 score0.011
Threshold uncertainty score0.022

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.000
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 head0.020
GPT teacher head0.266
Teacher spread0.247 · 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

Citations14
Published2012
Admission routes1
Has abstractyes

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