Field Pea Supplementation in Dairy Cattle - Supplementary data
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.133 | 0.035 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".