218 Quantifying resilience in growing pigs under a heavy disease challenge using daily individual feed intake records
Bibliographic record
Abstract
Abstract Resilience is defined as the ability to maintain productivity through any number of stressors such as disease or heat stress. A total of 2273 animals, in groups of ~60–75 piglets, were sent through a natural disease challenge barn every three weeks that consisted of three phases: i) a healthy quarantine nursery to collect immune parameters, ii) a challenge nursery, and iii) a challenged finishing unit which was attached to the challenge nursery. Individual feed intake (FI) was collected in the finishing unit with IVOG® feeders and aggregated into daily totals. Three resilience phenotypes were extracted from the individual trends in feed intake over time, including the root mean square error (RMSE), the quantile regression (QR), and run of depression (ROD) phenotypes. The RMSE phenotype was calculated by fitting a simple linear regression of FI on age within animal and taking the square root of the average squared residual from the model. To calculate the QR phenotype, a 5% quantile regression was fitted across all daily feed intake records to set a lower bound for off-feed days. The QR phenotype was quantified as the proportion of days within animal that fell below the overall quantile regression line. The ROD phenotype was calculated by fitting a within animal linear regression line, flagging extended consecutive stretches of days below that regression line (i.e. a ROD), and calculating the percentage of days that fall within a ROD for each animal. Heritability estimates for the FI resilience phenotypes ranged from 0.10±0.04 to 0.17±0.04. Genetic correlations of the FI resilience phenotypes with mortality and treatment rate ranged from 0.66±20 to 0.94±0.20. This research demonstrates that resilience phenotypes can effectively quantify resilience and can add value to a breeding program to improve resilience to many stressors. Funded by Genome Canada, Genome Alberta, and PigGen Canada.
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 | 0.001 | 0.001 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".