Identification of animal-based traits as indicators of production diseases in pigs
Bibliographic record
Abstract
Production diseases induce loss of performance associated with the reduction in growth, feed efficiency and product quality, and increase in mortality and morbidity. This impacts on the profitability of a pig farm and goes against citizen acceptability of animal production. This study consisted of a systematic review of the published literature on production diseases affecting digestive, locomotory, and respiratory systems in pigs. It aimed to quantify the effect of these diseases on traits used to measure animal response. Data were extracted from 67 peer-reviewed publications selected from 2,339 records that resulted from an exhaustive online keyword search using a search engine. Traits were classified as productive traits (growth), behavioural, carcass (composition or dimensions), biochemical (concentration of a marker), and molecular traits for measures relative to DNA, RNA and protein expression. A meta-analysis based on mixed models was performed on traits assessed more than 5 times across studies, using the package metafor of the R software. A total of 524 unique traits were recorded 1 to 31 times in a variety of sample material including blood, muscle, articular cartilage, bone, or at the level of the animal for productive traits. No behavioural traits were recorded overall from the included experiments. 17 traits were measured more than five times across studies. The heterogeneity (I2) within these traits and across studies was low (I2=0%) for Crypt depth, Inflammatory biomarkers, and Cytokines such as Interleukine (IL) 6 and 8 and high for Feed conversion ratio, Melatonin and other cytokines, such as IL 1-beta and tumor necrosis factor alpha (I2>60%). The traits mostly affected by the diseases were molecular and biochemical traits, specifically the inflammatory biomarkers Haptoglobin and Fibrinogen with effect sizes of 3.0 (Confidence interval (CI) 1.62-4.38) and 2.9 (CI 1.81-4.0), and cytokines such as IL 6 and 8, with effect sizes of 2.65 (CI 1.46-3.84) and 2.75 (CI 2.08-3.43), respectively. Average daily weight gain showed a summary effect size of -1.98 (CI -2.65 to -1.31) across studies. These traits can be considered as potential tools for the prognosis of production diseases or conversely to characterize healthy animals.
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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 | 0.012 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.012 |
| Bibliometrics | 0.014 | 0.010 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".