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Record W4401254827 · doi:10.6000/1927-520x.2024.13.09

Research of Species Composition of Bovine Piroplasmosis and its Distribution in the South of Kazakhstan

2024· article· en· W4401254827 on OpenAlexvenueno aff
Aisulu T. Kuzerbayeva, Kenes S. Baizhanov, Roza Zh. Ermekbayeva, Askar Zh. Userbai, Nurzhan O. Nurkhojayev

Bibliographic record

VenueJournal of Buffalo Science · 2024
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicVector-Borne Animal Diseases
Canadian institutionsnot available
Fundersnot available
KeywordsLivestockEpizooticContext (archaeology)GeographyDistribution (mathematics)PastureTickEcologyBiologyEnvironmental protectionArchaeology

Abstract

fetched live from OpenAlex

Context: The relevance of the stated subject of scientific research is determined by the need for rational planning and timely implementation of therapeutic and prophylactic measures with cattle in different geographical regions to prevent the spread of ticks and develop an objective understanding of the real features of the epizootic situation in these regions. Object: This scientific research aims to explore the species composition of bovine piroplasmosis and its distribution in the Turkestan region of the Republic of Kazakhstan. Methods: This research combines species identification of ixo did ticks with practical examination and regular collection from cattle during the pasture season to determine tick species and pasture tickiness, conducted at Mukhtar Auezov South Kazakhstan University, the regional veterinary laboratory, and farms in the Turkestan region. Results: During this scientific research, practical results were obtained, indicating the main trends in the species composition and development of dynamics of the distribution of Bovine theileriosis on the territory of the Turkestan region. The research results and conclusions are highly significant for livestock farm workers in the Turkestan region of Kazakhstan, aiding in cattle health management and epidemic prevention.

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)0.002
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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.920
Threshold uncertainty score0.180

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.065
GPT teacher head0.313
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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
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

Citations0
Published2024
Admission routes1
Has abstractyes

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