BREEDING AND GENETICS SYMPOSIUM: Resilience of livestock to changing environments1
Bibliographic record
Abstract
The Breeding and Genetics Symposium titled “Resilience of Livestock to Changing Environments” was held at the Joint Annual Meeting, July 19–24, 2016, in Salt Lake City, UT. The objective of the symposium was to provide a broad overview of recent research on the effects of changing environmental conditions on livestock. Topics covered by the speakers included a review of the variation in response to heat stress and its effects on metabolic parameters and energy demands in pigs and cattle, production and reproduction in livestock and aquaculture species, the development of genetic improvement programs to produce more robust animals, and the use of gene introgression to develop heat-resistant animals. Substantial discussion focused on the tradeoffs involved in producing robust, high-producing livestock. The symposium included 6 invited presentations, each of which is discussed below. Modern livestock have been selected to efficiently convert feed into food and fiber for human use, but the most productive breeds generally require intensive management to maintain high levels of production. Most major livestock breeds in the U.S. are derived from animals that evolved in temperate climates, such as Holstein dairy cattle. Unfortunately, the climate in the southern states is hot enough to cause several months per year of heat stress. Heat stress occurs when the environmental temperature exceeds an animal's thermoneutral point, and its effects include decreased dry matter intake, reduced water consumption, depressed production, and impaired fertility (e.g., West, 2003). These effects will become more common in areas that have not previously experienced heat stress as global temperatures continue to rise (IPCC, 2014). Technological interventions, including fans, sprinklers, and shade structures, can be used to ameliorate many of the effects of heat stress, but they provide only temporary relief. Genetic selection for greater thermotolerance is possible and will result in cumulative, permanent gains (Aguilar et al., 2009; Dikmen et al., 2012, 2015).
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 | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.024 | 0.007 |
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".