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Record W2565645302

Молочная продуктивность и адаптивная способность дочерей быков разного экогенеза

2016· article· ru· W2565645302 on OpenAlexaboutno aff
Четвертакова Елена Викторовна

Bibliographic record

VenueВестник Омского государственного аграрного университета · 2016
Typearticle
Languageru
FieldAgricultural and Biological Sciences
TopicAnimal Nutrition and Health
Canadian institutionsnot available
Fundersnot available
KeywordsButterfatBreedUdderLactationAnimal scienceHerdMilk fatCrossbreedBiologyFood scienceMastitisPregnancy
DOInot available

Abstract

fetched live from OpenAlex

The data on milk production of daughters of bulls of different breeds and ecogenesis. Analyzed milk yield, protein and fat content in milk of cows of breeding reproducer. A significant advantage of daughters of bulls of the Danish selection for the yield of milk and fat content in milk. For the yield of milk at first and second lactation, they exceeded the first group (control) on 457.4 kg (P > 0.999) and 487,8 kg (P > 0.99), butterfat 0.1% (P > 0.999) and 0.07% (P > 0.99), respectively. Daughters of bulls form the canadian selection was significantly inferior to the daughters of bulls of the red pied breed on the content of milk fat and milk protein. On fat content for first and second lactations of the daughters of the bulls form the canadian selection was inferior to the cows of the first group (control) by 0.03% (P > 0.95) and 0.14% (P > 0.999) and protein 0.04% (P > 0.99) and 0.06% (P > 0.99), respectively. Daughters of crossbred bulls was characterized by a higher content of protein in milk. Exceeded for this indicator cows of control group for first and second lactation by 0.1% (P > 0.999) and 0.07% (P > 0.999), respectively. The main reasons for disposal of cows from the herd: gynecological diseases, obstructed labour and complications after them, diseases of the udder and feet.

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)0.003
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesMeta-epidemiology (narrow), 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 score0.303
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0030.001
Meta-epidemiology (broad)0.0030.002
Bibliometrics0.0010.004
Science and technology studies0.0030.002
Scholarly communication0.0010.002
Open science0.0030.001
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0200.040

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 head0.045
GPT teacher head0.256
Teacher spread0.211 · 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

Citations0
Published2016
Admission routes1
Has abstractyes

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