MétaCan
Menu
Back to cohort
Record W2134306045 · doi:10.4141/cjas2010-041

Temperament in beef cattle: Methods of measurement and their relationship to production

2011· article· en· W2134306045 on OpenAlexafffundvenue
Thomas Sebastian, Jon Watts, J. M. Stookey, Fiona Buchanan, Cheryl Waldner

Bibliographic record

VenueCanadian Journal of Animal Science · 2011
Typearticle
Languageen
FieldVeterinary
TopicAnimal Behavior and Welfare Studies
Canadian institutionsUniversity of Saskatchewan
FundersUniversity of Saskatchewan
KeywordsTemperamentTraitPersonalityPsychologyStatisticsAnimal scienceMathematicsSocial psychologyComputer scienceBiology

Abstract

fetched live from OpenAlex

Sebastian, T., Watts, J. M., Stookey, J. M., Buchanan, F. and Waldner, C. 2011. Temperament in beef cattle: methods of measurement and their relationship to production. Can. J. Anim. Sci. 91: 557–565. Temperament is an individual trait influencing an animal's behavioural response to handling. This characteristic likely modulates the response of the animal to environments and social situations, and is perhaps best viewed as a component of its personality. We assessed temperament using three objective measuring tools, to determine if correlations exist between these and a traditional subjective evaluation. The tools used were strain gauges, a “movement measuring device” (MMD), and a chute exit timer. Four hundred steers were used. Exit time was correlated with values recorded with the MMD and absolute strain forces, and MMD values were related to absolute strain forces. When the animals were classified as “calm” or “wild” based on their subjective scoring, these two groups differed in their mean exit times, MMD values and absolute strain forces. The three objective measures yielded statistically correlated results between tests and across repetitions, and therefore may quantify correlated aspects of a personality trait (i.e., temperament). The objective scores were related to the traditional subjective score, but they provide the advantage of eliminating observer bias and may offer better tools for temperament selection. Significant positive relationships of daily gain with subjective score and MMD values indicate that traditional subjective scoring techniques can be replaced with more repeatable objective measures when temperaments are assessed for performance studies.

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)0.001
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 score0.599
Threshold uncertainty score0.773

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
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 head0.249
GPT teacher head0.375
Teacher spread0.125 · 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

Citations32
Published2011
Admission routes3
Has abstractyes

Explore more

Same venueCanadian Journal of Animal ScienceSame topicAnimal Behavior and Welfare StudiesFrench-language works237,207