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Record W2072404206 · doi:10.1071/ea05211

Impact of animal health and welfare research within the CRC for Cattle and Beef Quality on Australian beef production

2006· article· en· W2072404206 on OpenAlexaff
Ian G. Colditz, D.L. Watson, R. Kilgour, D. M. Ferguson, C. T. Prideaux, John R. Ruby, Peter D. Kirkland, Kevin F. Sullivan

Bibliographic record

VenueAustralian Journal of Experimental Agriculture · 2006
Typearticle
Languageen
FieldVeterinary
TopicAnimal Behavior and Welfare Studies
Canadian institutionsBell (Canada)
FundersNSW Department of Primary IndustriesCommonwealth Scientific and Industrial Research Organisation
KeywordsBeef cattleFeedlotWelfareAnimal welfareBusinessAnimal husbandryVeterinary medicineLivestockAgricultureBiotechnologyEnvironmental healthAgricultural scienceBiologyAnimal scienceMedicinePolitical scienceEcology

Abstract

fetched live from OpenAlex

Research within the health and welfare program of the Cooperative Research Centre for Cattle and Beef Quality has delivered important improvements to the Australian cattle industry. Vaccines to assist with the control of bovine respiratory disease were developed and commercialised from Australian isolates of Mannheimia haemolytica and pestivirus (mucosal disease). Our understanding of the benefits of weaning cattle by confinement and hand feeding in yards (yard weaning) has been consolidated, and yard weaning has been adopted as ‘best practice’ for cattle production in the temperate zones of Australia. The importance of good temperament for improved growth rates and reduced morbidity during feedlot finishing, and for adaptation to stressors such as road transport, has been demonstrated. In response to this knowledge, industry is increasingly measuring flight time for use in breeding programs and feedlot management. The risk to meat quality of stressors such as mixing unfamiliar cattle in the weeks preceding slaughter or acute stress in the last 15 min before slaughter has been described. Adoption of these findings through Quality Assurance schemes will assist in assurance for the community and for export markets of the welfare standards of the Australian cattle and beef industry. This review provides details of the experiments that led to these achievements and to some improved understandings of temperament and behaviour of beef cattle.

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)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.867
Threshold uncertainty score0.480

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.156
GPT teacher head0.463
Teacher spread0.306 · 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 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

Citations16
Published2006
Admission routes1
Has abstractyes

Explore more

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