Economic evaluations of beef bulls in an integrated supply chain1
Bibliographic record
Abstract
Economic benefits from the use of expected progeny of a sample of beef bulls with genetic evaluations were calculated over an integrated supply chain for combinations of price discounts for intramuscular fat and LM area. Fixed backfat finish and marketing at the point of optimized gross margins were considered. An economic model was used to calculate average expected gross margins for a sample of bulls. Across-breed, age-constant genetic evaluations were used to predict carcass characteristics of progeny including weight, retail yield, intramuscular fat, and LM area, as well as input requirements including feed and housing as a function of time on feed. Proportion of retail cuts affected by price discounts was included in the calculations. Optimizing endpoints did not affect rankings to any extent relative to a fixed end point in this sample of bulls, as a result of fixed endpoints being similar to optimized endpoints for the economic situation considered. However, rank correlations were only 0.63 and 0.71 between rankings for no discount being applied and rankings with discounts for intramuscular fat and LM area, for fixed and optimized endpoints, respectively. We conclude that market prices are necessary considerations in choices of bulls to use in commercial beef production.
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 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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".