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Record W2792768769 · doi:10.17221/3922-cjas

Association between aggressive behaviour and high-energy feeding level in beef cattle

2006· article· en· W2792768769 on OpenAlexaff
Y. Bozkurt, Serkan Özkaya, I. Ap Dewi

Bibliographic record

VenueCzech Journal of Animal Science · 2006
Typearticle
Languageen
FieldVeterinary
TopicAnimal Behavior and Welfare Studies
Canadian institutionsUniversity College of the North
Fundersnot available
KeywordsFeedlotAnimal scienceBeef cattleAnimal welfareSilageHigh energyBiologyBarnMealFood scienceGeographyEngineering

Abstract

fetched live from OpenAlex

The aim of this study was to investigate an association between aggressiveness and high level of feeding in a half-open feedlot production system. An experiment was conducted on 72 head of beef cattle of different breeds. The animals were at about 10 months of age. Medium quality silage was offered ad libitum and supplemented with high (HE) and low level (LE) of barley (2.5 and 1.5 kg/day/head, respectively) and supplemented without (nil) or with (+) extracted soybean meal (0.45 kg/day/head). Several types of animal behaviour were observed such as those parameters that are categorized to be main aggressive behaviours, butting, being butted, mounting and being mounted. Significant differences (P < 0.05) were found in butting, being butted behaviours between HE and LE treatment groups. Mounting and being mounted behaviours were significantly different (P < 0.05) in steers and heifers and between the seasons as well. Steers performed more incidents of mounting behaviour than heifers and it was the same for spring, during which animals had more mounting behaviours. It was concluded that there was a close relationship between high-energy diets and aggressive behaviour, which necessitates some management measures to be taken in order to ensure better animal welfare and beef production.

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.114
Threshold uncertainty score0.418

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 studies0.0000.000
Scholarly communication0.0000.001
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 head0.054
GPT teacher head0.324
Teacher spread0.270 · 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

Citations12
Published2006
Admission routes1
Has abstractyes

Explore more

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