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Record W4244853384 · doi:10.2458/azu_jrm_v54i4_majak

Practical measures for reducing risk of alfalfa bloat in cattle

2001· article· en· W4244853384 on OpenAlexaffabout
W. Majak, J. W. Hall, Tim A. McAllister

Bibliographic record

VenueJournal of Range Management · 2001
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicRuminant Nutrition and Digestive Physiology
Canadian institutionsAgriculture and Agri-Food Canada
Fundersnot available
KeywordsGrazingCultivarAgronomyBiologyRangelandLivestockForagePastureEcology

Abstract

fetched live from OpenAlex

Frothy bloat in cattle is a serious problem and is difficult to manage under field conditions as it progresses rapidly from early signs of distension to acute distress. Scientists at Agriculture and Agri-Food Canada centres in Western Canada are committed to the development of bloat-free alfalfa grazing systems, which may require feed additives or supplements. As well, a new cultivar of alfalfa (AC Grazeland), selected for a low initial rate of digestion, will soon be available. In grazing trials the cultivar reduced the incidence of bloat by an average of 56% compared with the control cultivar (Beaver). Commonly accepted mineral mixes for the prevention of bloat were tested and found ineffective but we have confirmed that poloxalene (Bloatguard®) is 100% effective if it is given intraruminally at the prescribed dose. However, under practical conditions, poloxalene can only be offered free choice and protection from bloat cannot be guaranteed. We have also shown that the water soluble polymer, Blocare® 4511, when used in the water supply is 100% effective in bloat prevention. This product is not yet registered in North America. Other strategies for bloat prevention will be discussed, including the selection of growth stages and grazing schedules, and the reduction of risk by wilting alfalfa or combining it with tannin-containing forages.

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)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.053
GPT teacher head0.293
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 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

Citations4
Published2001
Admission routes2
Has abstractyes

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