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Record W1908513371 · doi:10.4141/cjas2011-040

A comparison of commonly used and novel electronic techniques for evaluating cattle temperament

2012· article· en· W1908513371 on OpenAlexaffvenue
K. S. Schwartzkopf-Genswein, M. A. Shah, John S. Church, Derek B. Haley, Katharina Janzen, Gold Truong, Rowland Atkins, T.G. Crowe

Bibliographic record

VenueCanadian Journal of Animal Science · 2012
Typearticle
Languageen
FieldVeterinary
TopicAnimal Behavior and Welfare Studies
Canadian institutionsUniversity of SaskatchewanAgriculture and Agri-Food CanadaUniversity of GuelphLethbridge CollegeThompson Rivers UniversityAgriculture Food and Rural Development
Fundersnot available
KeywordsTemperamentAccelerometerStatisticsStrain gaugeLinear regressionRegression analysisMathematicsAnimal sciencePsychologyPhysicsPersonalityBiologyStructural engineeringSocial psychologyEngineering

Abstract

fetched live from OpenAlex

Schwartzkopf-Genswein, K. S., Shah, M. A., Church, J. S., Haley, D. B., Janzen, K., Truong, G., Atkins, R. P. and Crowe, T. J. 2012. A comparison of commonly used and novel electronic techniques for evaluating cattle temperament. Can. J. Anim. Sci. 92: 21–31. The temperament of steers (n=28) was assessed using five quantitative techniques including: flight time, flight distance, electronic (strain-gauge and accelerometer) tests, and three visual scores (VS) made during entry, restraint and exit from a squeeze chute. The objective of this study was to determine the most important predictive parameters based on those measurements and evaluate the relationship between the techniques. Flight time and distance were correlated with exit VS (r=−0.51, and 0.41, P<0.05; n=56), but were not related to restraint VS. Data from strain-gauge and accelerometer sensors were used to generate parameters such as peak response and area under the curve that were correlated with all three VS. Regression models using VS as the dependent variable and a combination of 2 to 5 parameters from the strain-gauge and accelerometer tests as independent variables predicted temperament with values of 29 to 65 or 41 to 57%, respectively. When all techniques, excluding VS, were used as independent variables, model accuracy increased to 72, 81 and 77% for restraint, exit and the sum of all VS, respectively. These findings suggest the objective measures of temperament assessed in this study could be used to identify highly reactive animals.

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 score0.778
Threshold uncertainty score0.294

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 communication0.0000.000
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 head0.178
GPT teacher head0.444
Teacher spread0.266 · 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

Citations14
Published2012
Admission routes2
Has abstractyes

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