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Prevalence of Fasciolosis in Cattle Farm of Tilottama Municipality, Rupandehi, Nepal

2022· article· en· W4312978416 on OpenAlexaff
Nishim Bhusal, Bhuwan Raj Bhatt, Saroj Shrestha, Arjun Chapagain

Bibliographic record

VenueVeterinary Sciences Research and Reviews · 2022
Typearticle
Languageen
FieldVeterinary
TopicHelminth infection and control
Canadian institutionsLambton College
Fundersnot available
KeywordsFasciolosisGeographyVeterinary medicineEnvironmental protectionFisheryAgroforestrySocioeconomicsEnvironmental scienceBiologyFasciola hepaticaHelminthsMedicineZoology

Abstract

fetched live from OpenAlex

Fasciolosis is a common parasitic disease affecting cattle and other ruminants, commonly sheep, and caused by Fasciola hepatica and F. gigantica. The disease is cosmopolitan in distribution and can cause extensive economic losses to the farmers. A cross-sectional study was conducted to determine the prevalence of fasciolosis in commercial cattle farms of Tilottama Municipality, Rupandehi district, Nepal. A total of 270 fresh faecal samples were collected purposively from the study area with different ages, sex, stage, and breeds for examination (sedimentation method) to visualize eggs of Fasciola microscopically. The obtained data were coded and analysed using Microsoft Excel 2016. The overall prevalence of fasciolosis in cattle was found to be 15.56%. Age and sex-wise prevalence was found to be statistically significant (P<0.05), while stage and breed-wise prevalence was insignificant (P>0.05). Fasciolosis is prevalent moderately among cattle in Tillottoma municipality, which necessitates the study of detailed epidemiology of the disease and effective control strategies to prevent huge economic losses.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.007
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.503
Threshold uncertainty score0.994

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0070.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.001
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 head0.410
GPT teacher head0.493
Teacher spread0.083 · 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 teacher head, 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

Citations1
Published2022
Admission routes1
Has abstractyes

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