MétaCan
Menu
Back to cohort
Record W4416142368 · doi:10.7868/s3034510325010057

Genetic evaluation of holstein cattle makes use of microsatellite DNA markers

2025· article· en· W4416142368 on OpenAlexaboutno aff
Л. А. Калашникова

Bibliographic record

VenueГенетика / Russian Journal of Genetics · 2025
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAgriculture and Biological Studies
Canadian institutionsnot available
Fundersnot available
KeywordsBreedAlleleHerdMicrosatelliteLoss of heterozygosityLocus (genetics)Genetic variationSelection (genetic algorithm)Holstein Cattle

Abstract

fetched live from OpenAlex

The results of a research of polymorphism of 12 micro-satellite loci in Holstein cattle from an ordinal number of regions of Russia and external countries were presented. The average number of alleles per locus was 5.43 ± 0.19, with variation in the range of 4–13 alleles, the average number of effective alleles was 3.26 ± 0.11. A list of 29 most frequent alleles has been fixed. 22 private alleles were identified, and the frequency of private alleles was 0.004–0.033. It has been demonstrated that the amount of locally alleles in domestic herds is higher than in animals of external selection. The mean level of observed heterozygosity for all loci hold at 0.681 ± 0.017 and varied in the range of 0.65–0.78 for a fixation index of –0.131 ± 0.005. Genetic length between herds of domestic selection were < 0.074. It was revealed that groups of cow herds come down into two clusters. The first cluster included animals from three areas of Russia, associated with bulls from Germany and the Netherlands, and the second cluster included individuals from other two provinces closest to the males of Canada, the USA and GB. At once, the oxen of Denmark and Finland founds themselves in a separate cluster. The basis of this work was to evaluate the allele reservoir of Holstein cattle of domestic selection and determine the genetic profile of the breed by STR markers.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.805
Threshold uncertainty score0.211

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 head0.042
GPT teacher head0.253
Teacher spread0.211 · 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

Citations0
Published2025
Admission routes1
Has abstractyes

Explore more

Same venueГенетика / Russian Journal of GeneticsSame topicAgriculture and Biological StudiesFrench-language works237,207