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Record W2071356967 · doi:10.2527/jas.2008-1729

Technical note: Correction of net portal absorption of nitrogen compounds for differences in methods: First step of a meta-analysis

2009· article· en· W2071356967 on OpenAlexaff
R. Martineau, Isabelle Ortigues Marty, J. Vernet, H. Lapierre

Bibliographic record

VenueJournal of Animal Science · 2009
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicAtmospheric chemistry and aerosols
Canadian institutionsAgriculture and Agri-Food Canada
Fundersnot available
KeywordsNet (polyhedron)NitrogenAbsorption (acoustics)MathematicsChemistryMaterials scienceOrganic chemistry

Abstract

fetched live from OpenAlex

The objective of this study was to evaluate the usefulness of correcting net portal absorption (NPA) of urea-N, ammonia, and AA-N for differences in methods before their inclusion into a meta-analysis. It was hypothesized that the difference, or portal-drained viscera (PDV) balance, between N inputs (apparently digested N plus urea-N) and outputs (ammonia plus AA-N) was 0 in the absence of measurement errors and based on the assumption that other sources of N inputs and outputs were relatively small and balanced each other. A database was built from 44 publications that reported data from 129 treatments (sheep, n = 71; beef cattle, n = 32; and dairy cows, n = 26). When necessary, NPA of urea-N (n = 38) and ammonia (n = 35) results were recalculated on a whole-blood basis, whereas NPA of AA-N (n = 87) was recalculated for all the N from AA transferred across the PDV rather than only the N from the alpha-amino group. Before corrections, PDV balance averaged 22.9% of N ingested (SD 29.0) for all treatments; after corrections, PDV balance significantly decreased to 10.2% of N ingested (SD 34.7). No difference in PDV balance was observed among species before or after corrections. Correcting NPA of urea-N, ammonia, and AA-N increased the accuracy without improving precision. Therefore, from a biological perspective, recalculating reported data seems appropriate to reduce bias due to differences in methods because this approach reduces the excess in N inputs relative to N outputs.

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)0.002
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 score0.651
Threshold uncertainty score0.235

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.047
GPT teacher head0.315
Teacher spread0.269 · 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

Citations10
Published2009
Admission routes1
Has abstractyes

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