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Record W4392862005 · doi:10.3390/ani14060894

Evaluation of Aberdeen Angus Breeding Bulls in the Northern Region of the Republic of Kazakhstan

2024· article· en· W4392862005 on OpenAlexaboutno aff
Pavel Shevchenko, Jan Miciński, Inna Brel-Kisselevа

Bibliographic record

VenueAnimals · 2024
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGenetic and phenotypic traits in livestock
Canadian institutionsnot available
FundersMinistry of Education and Science of the Republic of Kazakhstan
KeywordsGeographyAnimal scienceVeterinary medicineBiologyMedicine

Abstract

fetched live from OpenAlex

In this study, we evaluated the progeny quality of proven bulls of the Aberdeen Angus breed in different selections. Reliable information about the productive qualities of the daughters and cows of proven bulls is limited in Kazakhstan's scientific literature. We aimed to identify bull-improvers by comparatively evaluating cows of different genotypes in terms of productive qualities. The study was conducted in the period of 2021-2023, during which four groups of cows were formed based on origin. In conclusion, the high influence of inheritance of breeding features from bull-improvers should be highlighted: 78.6% to the daughters of cows of group I (Estonian selection from Kolos-firma LLP: EE 14465116 ABAJA PORTOS 95283, DK 1989501341 FREDERIK 95220, and EE 16966079 ABAJA PATRICK 95305) and 74.5% to the daughters of cows of group III (Canadian selection from Vishnevskoe LLP: BH BRUIN 54X 1644270 and JL DISTRICT 0311 1594050). There was less influence from proven bulls of deteriorators of the Kazakh selection: 21.4% to the daughters of cows of group II from Kolos-firma LLP (KZP156547191) and 25.5% to the daughters of cows of group IV from Vishnevskoe LLP (Henri KZT157789649 and Argus KZT155778964). Therefore, it is recommended that valuable genotypes of imported selection are used in breeding work in the Kostanay and North Kazakhstan regions for the improvement of Aberdeen Angus cattle.

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)0.001
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 score0.757
Threshold uncertainty score0.147

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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)0.0000.000

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 head0.034
GPT teacher head0.285
Teacher spread0.250 · 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

Citations1
Published2024
Admission routes1
Has abstractyes

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