A comparison of commonly used and novel electronic techniques for evaluating cattle temperament
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".