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Use of genetic markers of meat productivity in breeding of Hereford breed bulls

2019· article· en· W2988674548 on OpenAlexaboutno aff
М. П. Дубовскова, Marina Selionova, Людмила Николаевна Чижова, E. S. Surzhikova, Nikolay P Gerasimov, Антонина Кузьминична Михайленко, M A Dolgashova

Bibliographic record

VenueIOP Conference Series Earth and Environmental Science · 2019
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGenetic and phenotypic traits in livestock
Canadian institutionsnot available
Fundersnot available
KeywordsBiologyBreedHerdGenotypeGenotypingOffspringAlleleAnimal scienceProductivityBeef cattleGeneticsPopulationSelective breedingBiotechnologyGeneDemography

Abstract

fetched live from OpenAlex

Abstract The aim of the study was to assess the genetic potential in Canadian Hereford sires using DNA markers, identify complex genotypes and assess their impact on the growth, development and meat productivity of its offspring. Groups of sons were formed taking into account the complex genotypes of sires: group 1 ( n = 28) – sons of bulls carrying in their genome a complex of genotypes with desired alleles; group 2 ( n = 30) – sons of bulls with a complex of genotypes that lack the desired alleles. The offspring from bulls-carriers of the “desirable” alleles in complex CAPN1, GH, Lep, TG5 genes that meet the exterior requirements exceeded their peers in live weight ( P < 0.05), carcass weight ( P < 0.05) and muscle tissue ( P < 0.05). The maximum conversion rate of feed protein into product protein was also established in the group of sons from selected bulls. Thus, animal selection for body conformation type is advisable to combine with the herd genotyping for a complex of genotypes associated with different economically useful traits when creating highly efficient population of beef 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)0.000
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 score0.414
Threshold uncertainty score0.381

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
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 head0.013
GPT teacher head0.194
Teacher spread0.182 · 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

Citations7
Published2019
Admission routes1
Has abstractyes

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