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Record W2804171284 · doi:10.31210/visnyk2018.01.31

Удосконалення умов утримання кнурів закордонної селекції, вплив моціону на виробництво сперми

2018· article· uk· W2804171284 on OpenAlexaboutno aff
А. С. Федяєва

Bibliographic record

VenueВісник Полтавської державної аграрної академії · 2018
Typearticle
Languageuk
FieldAgricultural and Biological Sciences
TopicAgriculture and Biological Studies
Canadian institutionsnot available
Fundersnot available
KeywordsAgricultural scienceAgricultureQuality (philosophy)SemenBusinessBiotechnologyBiologyEcology

Abstract

fetched live from OpenAlex

У статті наведено матеріали про використання моціону та удосконалення технологічних умов утримання кнурів зарубіжної селекції. Також вивчено вплив моціону на виробництво та якість сперми в умовах діючого господарства ДП «Націонал Плюс» ПП «Націонал» Дніпропетровської області. Встановили, що впровадження моціону та удосконалення умов утримання кнурів закордонної селекції позитивно вплинуло на фізіологічний стан кнурів, що призвело до покращання якості сперми. В умовах господарства виявлено кращі зарубіжні генотипи кнурів-плідників, основними з яких є термінальні кнури лінії Macster (канадської селекції), які за всіма показниками переважали всі інші.
 In the article materials are given on the use of exercise and the improvement of technological conditions for keeping of boars of foreign selection. Also the impact of the exercise on the production and quality of semen under the conditions of the current economy of the state enterprise «National Plus» of the private enterprise «National» of the Dnipropetrovsk region was studied.
 We established that the introduction of the exercise and the improvement of the conditions of keeping of boars of foreign breeding positively influenced on the physiological state of the boars, which led to an improvement in the quality of the sperm. Also in the conditions of farming, the best genotypes of boars-producers are revealed. One of the main is the terminal boars of the Macster line (Canadian breeding), which in all respects exceeded all other boars.

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)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Scholarly communication, Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesMeta-epidemiology (narrow), Science and technology studies, Insufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.220
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0030.001
Meta-epidemiology (broad)0.0030.002
Bibliometrics0.0000.004
Science and technology studies0.0040.005
Scholarly communication0.0010.001
Open science0.0040.002
Research integrity0.0030.002
Insufficient payload (model declined to judge)0.0150.015

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 head0.025
GPT teacher head0.218
Teacher spread0.192 · 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; both teacher heads agree on what is shown here.

Study designNot applicable
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

Citations2
Published2018
Admission routes1
Has abstractyes

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