Performance and carcass characteristics when sorting feedlot cattle on the basis of phenotype, and leptin genotype along with differential use of β-adrenergic agonists
Bibliographic record
Abstract
Kononoff, P. J., Defoor, P. J., Engler, M. J., Swingle, R. S., James, S. Y., Deobald, H. M., Deobald, R. L., Woronuk, G. N. and Marquess, F. L. S. 2015. Performance and carcass characteristics when sorting feedlot cattle on the basis of phenotype, and leptin genotype along with differential use of β-adrenergic agonists. Can. J. Anim. Sci. 95: 455–463. Crossbred steers were used in a randomized complete block design to evaluate a feedlot cattle sorting system. The sorting system combined information on live body weight, ultrasound fat thickness, coat color, and genotype on the leptin R25C genotype. Using described sorting criteria, a total of four groups (Groups 1–4) of cattle were created along with a randomly selected unsorted control (Group 5), with Groups 1 through 5 representing experimental treatments, and placed in one of five pens within each block. Block was replicated 10 times thus n equaled 50. All cattle were administered zilpaterol hydrochloride except Group 3, which we hypothesized would have a greater proportion of cattle reaching a higher value Quality Grade, and as a result administered ractopamine hydrochloride. The initial body weight of cattle was similar (P=0.426) between the control Group and Groups 1–4. Initial fat, as measured by ultrasound, was also observed to be similar (P=0.256) between these two groups. Overall, when cattle included in Groups 1–4 were compared with Group 5, hot carcass weight was greater (P=0.040), while the proportion of excessively heavy weight carcasses was less (P=0.049). Additionally, hot carcass weight gain tended (P=0.096) to be higher for Groups 1–4 compared with Group 5. No differences (P≥0.129) were observed for calculated yield grade. On average, cattle included in the Sorting Groups 1–4 returned $23 more per head than those in Group 5.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".