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Record W4387408938 · doi:10.1139/cjas-2023-0053

<i>Silybum marianum</i> seed extract as a potential phytogenic feed additive for improving growth performance and nutrient digestibility in growing pigs

2023· article· en· W4387408938 on OpenAlexvenueno aff
MM Hossain, Sungbo Cho, In Ho Kim

Bibliographic record

VenueCanadian Journal of Animal Science · 2023
Typearticle
Languageen
FieldMedicine
TopicSilymarin and Mushroom Poisoning
Canadian institutionsnot available
Fundersnot available
KeywordsSilybum marianumMilk ThistleAnimal scienceDry matterBiologyNutrientFood scienceChemistryBotanyPharmacology

Abstract

fetched live from OpenAlex

Silymarin is the flavonoid extracted from Silybum marianum seed. It has anti-inflammatory and antibacterial properties, and it supports liver health. The growth performance parameters, nutrient digestibility, and blood profile in growing pigs fed the dietary Silybum marianum seed extract were analyzed in this study. A total of 196 crossbred growing pigs ((Yorkshire × Landrace) × Duroc) were allocated into three dietary groups: CON: basal diet; TRT1: basal diet with 0.05% Silybum marianum seed extract; and TRT2: basal diet with 0.10% Silybum marianum seed extract. Results showed that pigs fed with Silybum marianum seed extract up to 0.10% increased average daily gain ( p &lt; 0.10) and feed intake ( p &lt; 0.05). The digestibility of dry matter, nitrogen, and energy were increased linearly ( p &lt; 0.05) with addition of Silybum marianum seed extract up to 0.10%. However, serum bile acids, alanine aminotransferase (ALT), and aspertate aminotransferase (AST) were not changed ( p &lt; 0.05). In conclusion, the significant improvements in average daily gain, feed intake, and nutrient digestibility indicate that Silybum marianum seed extract can positively influence the growth of growing pigs. Finally, natural feed additives like Silybum marianum seed extract may be used as an efficacious growth promoter and ultimately contributing to sustainable pig farming.

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)0.001
Version: codex-gemma-dda1882f352aValidation 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 score0.669
Threshold uncertainty score0.531

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.014
GPT teacher head0.251
Teacher spread0.237 · 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 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

Citations8
Published2023
Admission routes1
Has abstractyes

Explore more

Same venueCanadian Journal of Animal ScienceSame topicSilymarin and Mushroom PoisoningFrench-language works237,207