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Record W2919347518 · doi:10.1139/cjas-2018-0159

Effects of dietary protected organic acids on growth performance, nutrient digestibility, fecal microflora, diarrhea score, and fecal gas emission in weanling pigs

2019· article· en· W2919347518 on OpenAlexvenueno aff
Yi Yang, K.Y. Lee, In Ho Kim

Bibliographic record

VenueCanadian Journal of Animal Science · 2019
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAnimal Nutrition and Physiology
Canadian institutionsnot available
Fundersnot available
KeywordsWeanlingFecesAnimal scienceDiarrheaBiologyNutrientDry matterFood scienceChemistryInternal medicineMedicineMicrobiologyEndocrinologyEcology

Abstract

fetched live from OpenAlex

A total of 112 weanling pigs with an average body weight of 6.70 ± 1.31 kg were allotted to one of four experimental diets for a 6 wk feeding trail to evaluate the effects of protected organic acids on growth performance, nutrient digestibility, and fecal microbial counts. Diets consisted of basal diet (CON), CON + 0.2% unprotected organic acid (UOA), CON + 0.1% protected organic acid (POA1), and CON + 0.2% POA (POA2). Feeding POA diets to weanling pigs increased (P < 0.05) the average daily gain (ADG) during 0–2 wk and overall experimental period, and it also increased (P < 0.05) the apparent total tract digestibility (ATTD) of dry matter (DM) compared with UOA. The addition of increasing levels of POA showed greater (linear effect, P < 0.05) ADG, gain/feed ratio, and ATTD of DM. In addition, dietary inclusion of increasing levels of POA linearly increased (P < 0.05) the fecal Lactobacillus counts, whereas the Escherichia coli and Salmonella counts, diarrhea score, fecal ammonia, and acetic acid emissions were reduced (linear effect, P < 0.05). In conclusion, dietary supplementation of 0.2% POA to weanling pigs has the potential to improve growth performance and reduce diarrhea incidence while balancing microbial counts.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.792
Threshold uncertainty score0.240

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.011
GPT teacher head0.201
Teacher spread0.190 · 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

Citations23
Published2019
Admission routes1
Has abstractyes

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