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Record W1975682384 · doi:10.5539/jas.v2n4p3

Characterizing Fecal and Manure Phosphorus from Pigs Fed Phytase Supplemented Diets

2010· article· en· W1975682384 on OpenAlexafffundvenue
Stephen Abioye, D. V. Ige, Oluwole Akinremi, Martins Nyachoti, Don Flaten

Bibliographic record

VenueJournal of Agricultural Science · 2010
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPhytase and its Applications
Canadian institutionsUniversity of Manitoba
FundersManitoba Rural Adaptation Council
KeywordsPhytaseFecesManurePhosphorusAnimal scienceUrineChemistryFood scienceBiologyAgronomyBiochemistryMicrobiology

Abstract

fetched live from OpenAlex

We conducted this study to characterize P forms in feces and manure from pigs fed phytase supplemented diets and to determine if higher phytase levels can result in greater reduction in manure P without increased P solubility. Twenty-eight growing pigs were fed diets containing varying levels of supplemental P and phytase. Phosphorus concentrations in feces, urine and manure were determined and fecal and manure P were fractionated. Phytase addition reduced P concentration in feces and manure but increased urine P concentration. The greatest significant reduction in fecal and manure P was in pigs fed diet containing 2000 U phytase kg-1 without supplemental P, with 33% reduction in manure P. Inorganic P constituted more than 85% of fecal and manure P and the percentage decreased with phytase addition. Our study showed that higher phytase levels up to 2000 U phytase kg-1 could offer additional advantage of reducing manure P concentration and solubility.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

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 head0.011
GPT teacher head0.227
Teacher spread0.216 · 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 source (direct Gemma or distilled Codex), 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

Citations13
Published2010
Admission routes3
Has abstractyes

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