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Record W2211976489 · doi:10.3382/ps/pev158

Effects of a silica-based feed supplement on performance, health, and litter quality of growing turkeys

2015· article· en· W2211976489 on OpenAlexaff
S. T. Tran, Mary Bowman, Terence K. Smith

Bibliographic record

VenuePoultry Science · 2015
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAnimal Nutrition and Physiology
Canadian institutionsUniversity of Guelph
Fundersnot available
KeywordsLitterAmmoniaPoultry litterEnvironmental scienceSilicon dioxideFeed conversion ratioVolatilisationAnimal scienceChemistryPulp and paper industryBiologyAgronomyNutrientBody weightMaterials scienceBiochemistry

Abstract

fetched live from OpenAlex

Poor litter quality is a potential challenge to footpad health as well as the primary cause of ammonia volatilization. High ambient ammonia concentration is one of the most significant factors negatively affecting poultry production today. Some minerals have been reported to reduce ammonia release from poultry litter. Silicon dioxide, a highly pure and natural mineral, shows promise in decreasing ammonia volatilization and improving litter quality. The objective of the current study was to investigate the effects of feed-borne silicon dioxide on litter quality and how this impacts bird performance, general health and footpad health throughout a 12-wk posthatching turkey study. Supplementing the diet with silicon dioxide was found to significantly improve turkey BW gain and the efficiency of feed conversion. The severity of footpad dermatitis was monitored throughout the experimental period but no significant effect of diet was seen. The feeding of silicon dioxide reduced litter pH which decreased the conversion of NH4⁺ to NH3 thereby reducing nitrogen losses from litter. It was concluded that, under our study conditions, the feeding of 0.02% silicon dioxide offers potential economic benefits to turkey producers.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.869
Threshold uncertainty score0.103

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.043
GPT teacher head0.291
Teacher spread0.247 · 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

Citations34
Published2015
Admission routes1
Has abstractyes

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