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Record W2171618076 · doi:10.3168/jds.2012-5380

Factors associated with ruminal pH at herd level

2012· article· en· W2171618076 on OpenAlexaff
T. Geishauser, N. Linhart, A. Neidl, Anette Reimann

Bibliographic record

VenueJournal of Dairy Science · 2012
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicRuminant Nutrition and Digestive Physiology
Canadian institutionsUniversity of Guelph
Fundersnot available
KeywordsRumenAnimal scienceHerdMorningLactationIce calvingBiologyChemistryFood sciencePregnancyBotanyFermentation

Abstract

fetched live from OpenAlex

The objective of this study was to evaluate factors associated with ruminal pH at herd level. Four hundred and thirty-two cows of a Thuringian dairy herd were sampled before claw trimming using a rumen fluid scoop. Volume and pH of the rumen sample were measured, and lactation number, percentage of concentrates in the ration, days in milk (DIM), time of day, and daily milk yield were recorded. Rumen sampling was successful in 99.8% of the cows. The average sample volume was 25 mL. Rumen sample pH decreased with increasing percentage of concentrates in the ration. Ruminal pH decreased from calving to 77 DIM, and grew subsequently to 330 DIM. During the day, rumen pH followed a sinus curve, with maxima in the morning (0915 h) and afternoon (1533 h), and a minimum around noon (1227 h). Ruminal pH decreased with increasing daily milk yield. Lactation number interacted with daily milk yield on rumen pH. The percentage of concentrates in the ration, DIM, time of day, and daily milk yield were significant factors affecting ruminal pH at the herd level.

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)0.001
metaresearch head score (Gemma)0.003
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.099
GPT teacher head0.272
Teacher spread0.172 · 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

Citations63
Published2012
Admission routes1
Has abstractyes

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