33 Quantifying Resilience from Individual Feed Intake Data in a Natural Disease Challenge Model for Growing Pigs.
Bibliographic record
Abstract
Quantifying resilience in a health challenged environment could be beneficial to add information in commercial crossbred testing systems beyond mortality. Mortality and treatment records can be biased due to the subjective nature of euthanizing or treating individuals. Mortality may also capture other problems (e.g. ruptures) that are not linked to disease resilience. Objective measures would help quantify resilience to disease and other stressors. Feed intake is sensitive to disease due to the physiological effects of illness on feed intake. To study resilience, a natural challenge model was set up in Quebec, Canada. Every three weeks, batches of ~60-75 F1 (Large White x Landrace) healthy weaned barrows were sent to a high health quarantine nursery to take pre-challenge samples for potential predictors of resilience and then sent to the challenge facility after ~3 weeks. From the first 1341 animals, two separate measures of resilience were calculated using individual daily feed intake. The first involved regressing daily feed intake (FI) or duration at the feeder (DUR) on age and extracting the root mean square error (RMSE) within individual (RMSEFI and RMSEDUR, respectively). The second measure was computed as the percentage of negative residuals for an animal from quantile regression of FI on age using the 0.05 quantile across animals (FIQR05), which were classified as sick days. Mortality (0/1) and treatment rate per day times 180 days (TRT180) were used to validate the resilience measures. Heritability estimates for RMSEFI, RMSEDUR, and FIQR05 were 0.22 (±0.07), 0.25 (±0.08), and 0.17 (±0.06), respectively. Heritability estimates for mortality and TRT180 were 0.13 (0.05) and 0.29 (0.07). The genetic correlation between mortality and TRT180 was 0.93 (±0.14). RMSEFI and RMSEDUR had a genetic correlation with mortality of 0.54 (±0.36) and 0.65 (±0.35), respectively. RMSEFI and RMSEDUR had genetic correlations with TRT180 of 0.56 (±0.20) and 0.64 (±0.14), respectively. FIQR05 showed positive genetic correlations of 0.40 (±0.40) and 0.87 (±0.10) with mortality and TRT180, respectively. FIQR05 also showed moderate to strong negative genetic correlations of -0.70 (±0.18) and -0.76 (±0.19) with finishing average daily gain (ADG) and average daily feed intake (ADFI). RMSE measures showed lower genetic correlations with ADG and ADFI (-0.31 ± 0.27 and -0.19 ± 0.26 for RMSEFI and RMSEDUR, respectively). This research demonstrates that resilience measures that are genetically correlated with mortality and treatment rate can be extracted from feed intake data. Funding from Genome Alberta (ALGP2), Genome Canada, and PigGen Canada.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".