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Production traits of pigs of intrabreed type “Pripyatsky” in Landrace breed at breeding enterprises

2024· article· en· W4400491776 on OpenAlexaboutno aff
И. П. Шейко, Р. И. Шейко, Н. В. Приступа, E. A. Yanovich, V. N. Zayats, Maria Krasovskaya

Bibliographic record

VenueDoklady of the National Academy of Sciences of Belarus · 2024
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAgriculture and Biological Studies
Canadian institutionsnot available
Fundersnot available
KeywordsBreedBiologyProduction (economics)Animal scienceAgronomyBiotechnologyAgricultural scienceEconomics

Abstract

fetched live from OpenAlex

To meet the population’s demand for meat pork, breeding programs aimed at breeding and selection of pigs with high reproductive, fattening and meat traits were carried out for several decades. Over the years, a number of new types, lines and breeds of pigs were created and tested in the world. It should be noted that the gene pool of foreign super-meat breeds (Pietrain; Duroc; Danish, French, German, Canadian Landrace and Yorkshire) was used to some extent in the development of new domestic meat genotypes of pigs. A rational use of the genetic potential of farm animals allows accelerating the selection to improve their reproductive, fattening and meat traits. The Republic of Belarus arranged its own production of high-value animals represented by intrabreed type-pigs in the Landrace breed with the following productivity indicators: prolificacy – 12.5 animal units, milk yield – 65.5 kg, number of piglets at weaning – 11.6 animal units, litter weight at weaning at the age of 30 days – 91.6 kg, meat content in carcass – 65–67 %, well adapted to the technological conditions of breeding and industrial complexes, providing the overall need of pig breeding in obtaining high-quality pork; saving foreign currency for import; the possibility of exporting finished products to CIS countries.

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 score0.771
Threshold uncertainty score0.260

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.055
GPT teacher head0.281
Teacher spread0.226 · 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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