Improving Market Selection for Fed Beef Cattle: The Value of Real‐Time Ultrasound and Relations Data
Bibliographic record
Abstract
The introduction of value‐based marketing has provided the industry with the means to price cattle based on their desired attributes and has provided an alternative marketing channel for producers to select. Gains can be made by selecting animals that will be “in the grid” for value‐based marketing channels while screening out animals that won't and sending them to dressed‐value or live‐weight marketing channels. This study estimates the gains from using real‐time ultrasound (RTU) as well as information on graded animal relations (i.e., animals that have the same parentage slaughtered and graded in previous years) to predict carcass quality and yield grades prior to slaughter. These predictions are used in an optimization model designed to select the marketing channel for individual animals that will maximize returns. The optimal marketing strategy from this study involves a mix of live‐weight, dressed‐weight and grid sales methods rather than marketing all of the animals together. The results suggest that increases in returns in the range of $0.61–27.26 per head from using relations data, $9.04‐16.75 per head from using RTU measures and $11.27‐27.93 per head from using both to selectively market beef animals. These estimates do not account for the gains that could be obtained from using RTU to improve market timing, i.e., to time when the animal will grade best.
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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 | 0.007 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".