MétaCan
Menu
Back to cohort
Record W4411159217 · doi:10.1051/bioconf/202517901017

Efficiency of using the immunomodulatory probiotic “Sibersil®” in feeding pigs

2025· article· en· W4411159217 on OpenAlexaff
N. P. Buryakov, A.A. Ksenofontova, Ksenofontov Da, А. Н. Швыдков, Yu. A. Gulyaeva, Alexey V. Tkachev⊥

Bibliographic record

VenueBIO Web of Conferences · 2025
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAnimal Nutrition and Health
Canadian institutionsDebiopharm Group (Canada)
Fundersnot available
KeywordsProbioticBiologyFood scienceBacteriaGenetics

Abstract

fetched live from OpenAlex

A comprehensive study was conducted of the effect of the feed additive “Sibersil®” when introduced into the diets of pigs during the growing and fattening period on the survival rate of the livestock, meat productivity indices, hematological and biochemical status of animal blood, palatability and feed conversion. It was found that the optimal dose of the probiotic feed additive “Sibersil®” when introduced into the diets of meat-producing pigs during the growing and fattening periods is 100-200 g per 1 ton of compound feed. The studied feed additive had a positive effect on the survival rate of the livestock, meat productivity of animals, palatability and feed conversion. A positive effect of the studied probiotic on protein metabolism was established. No negative effect on calcium and phosphorus metabolism in the internal environment of the body was found. Industrial use of the additive in different periods of pig rearing is assumed.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.002

Distilled classifier scores by category (both heads)

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)0.0010.000

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 head0.044
GPT teacher head0.273
Teacher spread0.229 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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
Published2025
Admission routes1
Has abstractyes

Explore more

Same venueBIO Web of ConferencesSame topicAnimal Nutrition and HealthFrench-language works237,207