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Record W4413894998 · doi:10.3390/biom15091257

In Vitro and In Vivo Comparisons of Activated Charcoal and Biochar as Dietary Treatments for Controlling Boar Taint

2025· article· en· W4413894998 on OpenAlexafffund
Melissa Parent, Christine Bone, Lee‐Anne Huber, E. James Squires

Bibliographic record

VenueBiomolecules · 2025
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicMeat and Animal Product Quality
Canadian institutionsUniversity of Guelph
FundersCanada First Research Excellence FundNatural Sciences and Engineering Research Council of CanadaSwine Innovation Porc
KeywordsBiocharActivated charcoalCharcoalChemistryIn vivoIn vitroBoar taintFood scienceAnimal scienceBiologyBiotechnologyBiochemistryPyrolysisOrganic chemistryAdsorption

Abstract

fetched live from OpenAlex

Activated charcoal (AC) is an adsorbent that can prevent the accumulation of boar taint-causing compounds in the fat, but is not an approved dietary additive for livestock animals. Biochar (BC) is a similar feed-approved charcoal adsorbent that may be an alternative dietary additive to control boar taint. This study was conducted to evaluate AC and BC, both in vitro and in vivo, as dietary treatments for boar taint. This was done by first conducting an in vitro binding study to compare binding between AC, BC, and spent filter aid (SFA) for boar taint compounds. Results of the in vitro study showed that both AC and BC had significantly higher Bmax for androstenone (AC: 97.2 ± 0.4% and BC: 84.5 ± 0.8%) and skatole (AC: 106.1 ± 0.2%, BC: 113.2 ± 0.7%), compared to SFA with a Bmax of 50.5 ± 0.2% for androstenone and 97.1 ± 5.3% for skatole. AC and BC were then tested as feed additives in finisher diets fed to slaughter weight boars. Both adsorbents were successful at preventing boar taint in a subset of animals (83%), while having no effect on plasma levels of estrone sulfate or androstenone, and growth and performance parameters. These findings suggest that BC is a suitable alternative for AC as a dietary additive to prevent boar taint.

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)0.000
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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.043
Threshold uncertainty score0.150

Codex and Gemma teacher scores by category

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)0.0000.000

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 head0.033
GPT teacher head0.289
Teacher spread0.256 · 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

Citations1
Published2025
Admission routes2
Has abstractyes

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