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Record W2156968896 · doi:10.4141/p99-051

Bloat in cattle grazing alfalfa cultivars selected for a low initial rate of digestion: A review

2000· review· en· W2156968896 on OpenAlexvenueno aff
B. P. Berg, W. Majak, Tim A. McAllister, John W. Hall, D. H. McCartney, Bruce Coulman, B. P. Goplen, S. N. Acharya, R. M. TAIT, K.-J. Cheng

Bibliographic record

VenueCanadian Journal of Plant Science · 2000
Typereview
Languageen
FieldAgricultural and Biological Sciences
TopicRuminant Nutrition and Digestive Physiology
Canadian institutionsnot available
Fundersnot available
KeywordsPastureGrazingBiologyCultivarAgronomyBreedLivestockLegumeDigestion (alchemy)ForageAnimal scienceEcology

Abstract

fetched live from OpenAlex

The occurrence of frothy bloat limits the practice of alfalfa grazing in spite of the availability of strains bred specifically for pasture. Bloat is a chronic condition, endemic to cattle. Prophylactics and management techniques are available to reduce its incidence but they are expensive, difficult to administer, conflict with traditional grazing management regimens and do not eliminate bloat in all circumstances. A program to breed and evaluate a bloat-reduced strain of alfalfa was initiated in 1980 to overcome some of these limitations. A review of the results of grazing and feeding trials using alfalfas with low initial rates of digestion (LIRD) shows that this new strain reduces the incidence and severity of frothy bloat on pasture. Their effectiveness in controlling bloat was related to feeding or grazing management practices, the maturity of the plants and the season of use. Graziers may reduce the risk of occasional livestock losses from bloat by using LIRD cultivars, like AC Grazeland, or managing species/cultivar mixtures in ways that reduce the initial rate of digestion. Other bloat preventive strategies, including co-seeding with bloat-free legumes and using bloat-controlling prophylactics in combination with a LIRD alfalfa, are being investigated. Key words: Bloat, alfalfa, lucerne, legume, low initial rates of digestion, cattle, grazing

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.982
Threshold uncertainty score0.486

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.002
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 head0.053
GPT teacher head0.297
Teacher spread0.244 · 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 designOther design
Domainnot available
GenreReview

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

Citations45
Published2000
Admission routes1
Has abstractyes

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