Bibliographic record
Abstract
Genomics promises tremendous opportunity to the beef industry; however, that opportunity is currently stymied by the structure of the industry and the limited use of long-standing traditional genetic evaluations. Industry segmentation and poor, or in some cases lack of, market signals can be seen in the industry response of the past decade to a demand for more highly marbled beef. From 2005 to 2015, the feeding sector simply overfed animals to achieve higher marbling at the very great expense of excess fat, poor carcass yield, and poor feed efficiency. Although a logical decision in itself at the finishing level, this approach ignored the potential of “supply-chain” genetics to meet an end goal. A further persistent need that the beef supply must address is inconsistency of tenderness of beef at the consumer level. Beef is a premium protein product and, as such, must meet a higher standard for consumer satisfaction. Considering the heritability of tenderness, it would seem obvious as a supply-chain breeding goal. And yet no selection has been attempted, due in part to the nature of the trait: difficult to measure on breeding animals. This is an area of particular interest to make use of genomics. A simple DNA test can give an estimate for tenderness, which, applied to three generations of sire selection, could have a dramatic impact on consumer satisfaction. A third trait that should be of interest to every beef producer is feed efficiency. Although a great deal of focus has been placed on residual feed intake and affordable panels have been developed, little selection pressure has been brought to bear. Again, this is due, in large part, to the segmentation of the beef industry. Genetic improvement in the beef industry will only reach its potential following a fundamental shift in outlook. Current segmentation by sector and “ranch-level” genetics must be replaced with a more holistic approach in which information and market signals flow up and down the supply chain. Only then will producers and, more importantly, consumers benefit from the promise of genomics.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.899 | 0.812 |
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".