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Record W2589277088 · doi:10.12737/24372

MEAT QUALITY OF DIFFERENT GENOTYPES OF HEREFORD BREED CALVES

2017· article· en· W2589277088 on OpenAlexaboutno aff
Ismagil Hakimov, Almagul Givalbaeva

Bibliographic record

VenueBulletin Samara State Agricultural Academy · 2017
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicFood Industry and Aquatic Biology
Canadian institutionsnot available
Fundersnot available
KeywordsBreedGenotypeBiologyAnimal scienceQuality (philosophy)Veterinary medicineGeneticsMedicineGene

Abstract

fetched live from OpenAlex

The purpose of the research is Hereford breed calves meat quality improvement by harnessing the potential of the canadian selection bulls. Experiments were performed in LLC «K. H. Polanskoe» Bolshechernigovsk district of Samara region. The subject of investigations was the carcasses of Hereford steers of the breed obtained in the cows insemination of populations of canadian selection bulls stair: Wide Load 391W (group 1), upper Cut 20U (group 2), absolute 49S (group 3) and the descendants of domestic breeding bulls (control group). It was found that the calves of Hereford breed have good meat qualities. Exit steam carcasses in all groups was not less than 56.0% and the yield of pulp – not less than 79.0%. In calves, obtained from canadian selection bulls , meat quality was better expressed. The descendants of imported bulls by mass of steam carcass exceeded the indicators of the descendants of domestic bulls by the figure of 3.7-7.0% on exit of the flesh – 4.3 to 7.7%, they also had the advantage of index meat 2.1-4.2 percent. Canadian Herefords bulls were characterized by a greater mass of most valuable cuts of the carcass. They have plenty of hip part was 4.2 to 10.7 kg more than the domestic descendents. In the relative magnitude of this difference was 13.6 and 5.3%. The researches have allowed to draw a conclusion about expediency of use of canadian selection sires Hereford for improvement of meat quality of local Hereford farms, where this species are bred.

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 score0.870
Threshold uncertainty score0.931

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.064
GPT teacher head0.282
Teacher spread0.218 · 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
Published2017
Admission routes1
Has abstractyes

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