Optimizing a beef production system using specialized sire and dam lines
Bibliographic record
Abstract
Tang, G., Stewart-Smith, J., Plastow, G., Moore, S., Basarab, J., MacNeil, M. D. and Wang, Z. 2011. Optimizing a beef production system using specialized sire and dam lines. Can. J. Anim. Sci. 91: 353–361. Crossbreeding is an effective method for improving the efficiency of production in commercial cow-calf operations. It exploits available heterosis (hybrid vigour) and complementarity between different breeds or populations (lines). Before adopting a crossbreeding system, commercial cattle producers should evaluate available genetic resources and feasible crossbreeding systems, and choose one that is most beneficial for their own environment, resources, and management. This study compared profitability of alternative crossbreeding systems based on Beefbooster beef cattle breeding strains through computer simulation. Biological and economic data were collected from commercial customers of Beefbooster in Montana and western Canada, and breeding records from the database of Beefbooster, Inc. Three maternal strains (M1, M2 and M4) and two specialized paternal strains (M3 and TX), were evaluated with two simulated crossbreeding systems. System 1 uses a rotational cross between M1 and M4 with yearling crossbred heifers bred to M3 sires. System 2 is based on a three-strain rotation of M1, M2 and M4 with yearling crossbred heifers bred to M3 to facilitate ease of calving and crossbred cows bred to a classical terminal sire strain TX. Simulated base profit from system 2 was $29.57 greater ($215.21 vs. $185.64 yr−1 per cow) than from system 1.
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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 | 0.001 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".