Bibliographic record
Abstract
Abstract Efficiency in swine production is made up of a number of traits including reproduction, health, and the conversion of feed into pork. Due to the large proportion of cost associated with feed (>70% of overall costs) there has been a focus on this aspect of efficiency. Indeed, significant progress has been made in reducing the amount of feed required to reach market weight. This has been achieved at the same time as increasing growth rate (and therefore decreasing age at slaughter) whilst maintaining carcass quality to meet market needs. A significant proportion of this improvement was delivered through genetics and the application of new measurement technologies. Examples, include the use of ultrasound to measure fat content on the live animal and individual feed intake recording. At a time when sustainable production is increasingly demanded then efficiency will continue to be important through its impact on the economics of farming – productivity and profit. However, sustainability takes into account other aspects such as the impact on the environment as well as social aspects such as animal welfare. Many of these components support each other, for example, more prolific sows producing more efficient full market value pigs contribute to a smaller environmental footprint (more product and less waste). Likewise pigs that stay healthy even when responding to infection continue to eat and require less medication. When antagonisms exist they can be addressed in a balanced selection program that addresses all aspects. We are now at a point where new technologies will make it feasible to address these additional factors providing the opportunity for even greater progress in these key traits in the near future. This presentation will provide examples of the progress made and the role of genomics in utilizing “big data” to continue to improve the efficiency of swine production.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.016 | 0.005 |
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".