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DISTRIBUTION OF BULLS OF BREEDING ENTERPRISES OF THE RF BY BLOOD LEVEL OF RELATED BREEDS OF AYRSHIRE DAIRY CATTLE GROUP

2020· article· en· W3013001890 on OpenAlexaboutno aff
Е. Н. Васильева

Bibliographic record

VenueGenetics and breeding of animals · 2020
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGenetic and phenotypic traits in livestock
Canadian institutionsnot available
Fundersnot available
KeywordsPedigree chartBreedAnimal scienceVeterinary medicineBrown SwissBiologyDairy cattleMedicineGenetics

Abstract

fetched live from OpenAlex

He significance of blood count of related breeds of the Ayrshire group of dairy cattle investigated. Information processed on the pedigrees of 181 bulls whose sperm found in 10 breeding enterprises of the RF. The average blood pressure of bulls in the Finnish Ayrshire breed was 54.2%, Swedish red - 9.9%, Norwegian red - 8.8%, Canadian - 25.7% and others - 1.4%. Bulls distributed according to the presence in their pedigrees of blood residues of related breeds of the Ayrshire group in the age aspect depending on the place of their birth and breeding enterprises, and the level of breeding value by milk of their daughters and pedigree. Most of the bulls were born in Russia and Finland (45.9 and 46.4% respectively) in their pedigrees the Finnish Ayrshire breed predominates. The ranking of bulls by official breeding value (BV) and by pedigree (BV PED ) revealed that in the best bulls according to these indicators (+100 kg and more) bloodline prevails in pedigrees by Finnish Ayrshire breed compared to the worst group (less than -100 kg). This regularity noted in pedigrees in 57.2 and 77.6% of bulls, depending on the assessment options. The BV of bulls and predicted positively and reliably correlates with the presence of Finnish blood in the pedigree (+ 0.233 * and + 0.308 **) and significantly negatively with Canadian (-0.252 * and -0.467 **). The findings are consistent with previous 2013 studies on the best rated Viking Genetics bulls.

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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.138
Threshold uncertainty score0.532

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 studies0.0000.000
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 head0.030
GPT teacher head0.233
Teacher spread0.203 · 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
Published2020
Admission routes1
Has abstractyes

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