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Record W7111324818 · doi:10.17632/ch4kgmkzmb.1

Field Pea Supplementation in Dairy Cattle - Supplementary data

2025· dataset· W7111324818 on OpenAlexaboutno aff

Bibliographic record

VenueMendeley Data · 2025
Typedataset
Language
Field
Topic
Canadian institutionsnot available
Fundersnot available
KeywordsRumenDairy cattleDry matterStarchFermentationLivestockCattle feedingForage

Abstract

fetched live from OpenAlex

The Supplementary data provided in this repository include Supplemental Figures and Tables, as well as metadatafile required for reproducing the bioinformatics analyses on the 16S rRNA gene sequencing dataset. The title of this manuscript: Impact of dietary inclusion of field peas (Pisum sativum) on milk production, blood metabolites, rumen fermentation, and composition of rumen bacterial community of lactating dairy cows". The Northern Plains of Canada and the United States provide a suitable environment for growing field peas. Due to their high CP and starch concentrations, the livestock feed industry represents a potential market for lower-grade peas and pea by/co-products unsuitable for human consumption. This study evaluated the effects of partially replacing different protein sources in a corn grain-based concentrate with coarsely ground field peas in the diet of lactating dairy cows on milk production, blood metabolites, rumen fermentation, and rumen bacterial community. Inclusion of up to 7.8% coarsely ground peas increased rumen NH3-N, BUN, and MUN concentrations, and decreased digestibility of DM, CP, and NDF, but these changes did not negatively affect feed intake, milk yield and protein production, or rumen fermentation parameters. The results suggest that using a finer grind of peas may enhance digestibility and nutrient utilization in dairy cow diets.

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)0.007
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Open science, Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesMeta-epidemiology (narrow), Open science, Insufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Dataset · Consensus signal: Dataset
Teacher disagreement score0.165
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0070.002
Meta-epidemiology (narrow)0.0020.003
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0030.003
Science and technology studies0.0010.000
Scholarly communication0.0010.005
Open science0.0400.074
Research integrity0.0010.004
Insufficient payload (model declined to judge)0.1000.003

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 head0.091
GPT teacher head0.391
Teacher spread0.299 · 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; both teacher heads agree on what is shown here.

Study designNot applicable
Domainnot available
GenreDataset

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

Citations0
Published2025
Admission routes1
Has abstractyes

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