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BREEDING CHARACTERISTICS OF PRIZE COWS SIRES’DECENDENTS

2024· article· en· W4404089858 on OpenAlexaboutno aff
V. Yu. Sidorova

Bibliographic record

VenueSCIENTIFIC LIFE · 2024
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGenetic and phenotypic traits in livestock
Canadian institutionsnot available
Fundersnot available
KeywordsAnimal scienceBiology

Abstract

fetched live from OpenAlex

The impact on the cattle herds’ gene pool depends on the genetic contribution of such categories of breeding animals as fathers of cows (OK), mothers of cows (MK), fathers of fathers of cows (OOK), mothers of fathers (MO), fathers of mothers (OM), so on. The purpose of the study was the regularity of the appearance of record cows in various categories of breeding animals based on the data of the exhibitions "Stars of the Moscow region" in the adjacent 2018-2019 years’ determining. A total of 111 dairy cows were analyzed. It was found that the record cows in the offspring of high breeding value sires of specialized dairy breeds’ various categories of animals appearance is a random phenomenon: they can be both daughters of sires (O) and granddaughters (OM). As studies have shown, the characteristics of milk productivity (O) of producers (n= 2) who had record-breaking daughters (n=5) are as follows: lactation by the count of 1.8; number of days of lactation 302.6; milk yield of 9089.9 kg of milk with a fat content of 4.65%, protein content of 3.3%. 58 recordists participated in the exhibition "Stars of the Moscow region-2018". Of the total number of bulls, fathers of these cows, only 4 of them (6.1%) had more than one daughter: Nog-Badus-M 4900459 (3 daughters); Suban-M 107522499 (2 daughters); Image-M 50421237 (2 daughters); Montreal-M 50122424 (2 daughters). The productivity of the daughters of one of these bulls of the Image-M 50421237, amounted to 29 kg of daily milk yield, that is, 2.3 kg less than the average for the population of recordists (n= 26), whose productivity is 31.3 kg of milk.

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 score0.674
Threshold uncertainty score0.387

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 head0.017
GPT teacher head0.254
Teacher spread0.237 · 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
Published2024
Admission routes1
Has abstractyes

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Same venueSCIENTIFIC LIFESame topicGenetic and phenotypic traits in livestockFrench-language works237,207