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Record W3014220165 · doi:10.31838/srp.2020.3.71

Realization of the Genetic Potential of Imported Holstein Cattle in Agricultural Enterprises of Primorsky Krai of Russia

2020· article· en· W3014220165 on OpenAlexaboutno aff
Guli Koltun, Svetlana Terebova, Victoria V. Podvalova, Irina I. Shulepova, Aleksander N. Belov

Bibliographic record

VenueSystematic Reviews in Pharmacy · 2020
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicFood Industry and Aquatic Biology
Canadian institutionsnot available
Fundersnot available
KeywordsAgricultureAnimal husbandryDairy cattleBusinessFodderAgricultural scienceBreedLivestockAgricultural economicsBiologyGeographyAnimal scienceAgronomyEconomicsForestryEcology

Abstract

fetched live from OpenAlex

Currently the main goal of Russian agricultural industry is to provide the citizens with high-quality competitive domestic production. This goal can be also achieved with the help of dairy husbandry development. Production of high-quality dairy products depends on many factors: fodder supply, housing, climate factors and the genetic potential of animals. Improvement of cattle gene pool in farms of Primorsky Krai is provided by import of foreign highly-productive Holstein cattle from Northern America, Canada, Europe (Germany, Hungary, Holland). The experience of working with foreign dairy cattle in Khankaysky agro-industrial complex “GreenAgro” is represented in the article. The analysis of “GreenAgro”`s work let make a number of recommendations for farm enterprises and other forms of agricultural enterprises, which plan to run dairying. These recommendations are development of homegrown fodder supply for high-yielding dairy cows, using the semen of Holstein breeding bulls for insemination of local cattle for the purpose of getting offspring with 50 % chance of being pure blood and improvement of breed characteristics of local 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)0.001
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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.544
Threshold uncertainty score0.142

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
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 head0.064
GPT teacher head0.288
Teacher spread0.224 · 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
Published2020
Admission routes1
Has abstractyes

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