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Record W1969154944 · doi:10.1021/jf071588x

Chemical Structures of Manure from Conventional and Phytase Transgenic Pigs Investigated by Advanced Solid-State NMR Spectroscopy

2007· article· en· W1969154944 on OpenAlexaff
Jingdong Mao, A. Ajakaiye, Yeqing Lan, Dan C. Olk, Michael Warren Ceballos, Tiequan Zhang, Ming Fan, Cecil W. Forsberg

Bibliographic record

VenueJournal of Agricultural and Food Chemistry · 2007
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPhytase and its Applications
Canadian institutionsUniversity of Guelph
Fundersnot available
KeywordsPhytaseNuclear magnetic resonance spectroscopyManureSpectroscopyChemistrySolid-stateSolid-state nuclear magnetic resonanceBiotechnologyAgronomyBiologyStereochemistryPhosphorusOrganic chemistryNuclear magnetic resonancePhysical chemistry

Abstract

fetched live from OpenAlex

Nonpoint phosphorus (P) pollution from animal manure is becoming a serious global problem. The current solution for the swine industry includes the enzyme phytase as a component in oil meal and cereal grain-based swine diets. A long-term approach is the production of transgenic phytase pigs that express phytase in the salivary glands and secrete it in the saliva. This study provides a detailed comparison of chemical structures of manure from conventional pigs and transgenic pigs that express phytase under growing and finishing phases using new solid-state NMR techniques. Spectral editing techniques and quantitative NMR techniques were used to identify and quantify specific functional groups. Two-dimensional (1)H- (13)C heteronuclear correlation NMR was used to detect their connectivity. Manure from conventional and transgenic pigs had similar peptide, carbohydrate, and fatty acid components, while those from transgenic pigs contained more carbohydrates and fewer nonpolar alkyls. There was no consistent effect from diets with or without supplemental phosphate or growth stages.

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 score0.086
Threshold uncertainty score0.219

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 head0.007
GPT teacher head0.213
Teacher spread0.206 · 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

Citations15
Published2007
Admission routes1
Has abstractyes

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