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Record W2039136074 · doi:10.3168/jds.2007-0535

Ruminal Temperature May Aid in the Detection of Subacute Ruminal Acidosis

2007· article· en· W2039136074 on OpenAlexaff
Ousama AlZahal, E. Kebreab, J. France, M.A. Froetschel, B.W. McBride

Bibliographic record

VenueJournal of Dairy Science · 2007
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicRuminant Nutrition and Digestive Physiology
Canadian institutionsUniversity of ManitobaUniversity of Guelph
Fundersnot available
KeywordsAcidosisAnimal scienceRumenChemistryFood scienceMedicineBiologyInternal medicineFermentation

Abstract

fetched live from OpenAlex

The objective of this study was to investigate the relationship between ruminal pH and ruminal temperature and to develop a predictive equation that can aid in the diagnosis of subacute ruminal acidosis (SARA). Six rumen-fistulated lactating Holstein dairy cows (639 +/- 51 kg body weight) were used in the study. Cows were randomly allocated to 1 of 2 dietary treatments: control (% of dry matter, 40% corn silage, 27% mixed haylage, 7% alfalfa hay, 18% protein supplement, 4% ground corn, and 4% wheat bran) or SARA total mixed ration (% of dry matter, 31% corn silage, 20% mixed haylage, 5% alfalfa hay, 15% protein supplement, 19% ground wheat, and 10% ground barley) and were fed daily at 0700 and 1300 h. The experiment consisted of 1 wk of adaptation followed by 1 wk of treatment. Ruminal pH and ruminal temperature were simultaneously and continuously recorded every minute for 4 d per week using the same indwelling electrode. Subacute-acidotic cows spent more time (min/d) below ruminal pH 5.6 and a greater time above 39.2 degrees C than control cows. Ruminal pH nadir had a negative relationship with its corresponding ruminal temperature (R2 = 0.77). Therefore, ruminal temperature may have potential to predict ruminal pH and thus aid in the diagnosis of SARA.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.782
Threshold uncertainty score0.153

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.018
GPT teacher head0.256
Teacher spread0.239 · 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

Citations109
Published2007
Admission routes1
Has abstractyes

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