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Record W2785826224 · doi:10.1139/cjas-2017-0053

Effects of a matrix-coated organic acids and medium-chain fatty acids blend on performance, and in vitro fecal noxious gas emissions in growing pigs fed in-feed antibiotic-free diets

2018· article· en· W2785826224 on OpenAlexaffvenue
M.M. Hossain, Balachander Jayaraman, S.C. Kim, K.Y. Lee, In Ho Kim, C. M. Nyachoti

Bibliographic record

VenueCanadian Journal of Animal Science · 2018
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAnimal Nutrition and Physiology
Canadian institutionsUniversity of Manitoba
FundersZoetis
KeywordsFecesChemistryDry matterAnimal scienceUreaFactorial experimentFeed conversion ratioAcetic acidFood scienceFatty acidBody weightBiologyBiochemistryEndocrinologyMicrobiology

Abstract

fetched live from OpenAlex

This study evaluated the efficacy of a matrix-coated organic acids and medium-chain fatty acids blend (MCOFA) in growing pigs. Ninety six pigs [(Yorkshire × Landrace) × Duroc] with an average body weight (BW) of 47.71 ± 3.73 kg were used in a 6 wk experiment. Pigs were allotted to diets containing 0 or 2 g kg −1 of MCOFA, and 0 or 2.5 g kg −1 of antibiotic growth promoters (AGP) according to a 2 × 2 factorial arrangement of treatments. Pigs fed diets supplemented with MCOFA had improved growth efficiency compared with those fed a diet without MCOFA (P < 0.05). Pigs receiving the diets supplemented with both AGP and MCOFA had higher apparent total tract digestibility of crude protein, dry matter, fat, and gross energy (P < 0.05). Pigs fed AGP × MCOFA diet had increased serum urea nitrogen (P < 0.05). Pigs fed diets supplemented with AGP had reduced fecal ammonia (NH 3 ) gas emissions compared with those fed without AGP (P < 0.05). Moreover, pigs fed diets supplemented with MCOFA had reduced fecal NH 3 and acetic acid gas emissions compared with those fed without MCOFA (P < 0.05). In conclusion, dietary supplementation with MCOFA improved performance in growing pigs.

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)0.000
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.847
Threshold uncertainty score0.549

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
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 head0.009
GPT teacher head0.218
Teacher spread0.209 · 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

Citations14
Published2018
Admission routes2
Has abstractyes

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