Effect of health management strategy during rearing on grower-finisher pig performance and selected indicators of immune system stimulation
Bibliographic record
Abstract
The effects of health management strategy during rearing on subsequent growth performance and indicators of immune system stimulation in grower-finisher (G-F) pigs were evaluated. Pigs of approximately 25 kg body weight (BW) were sourced from three different management systems: (1) a farrow-to-finish unit with identified respiratory problems (Conventional), (2) segregated early weaned (SEW; piglets sourced from multiple sow herds), and (3) a minimal disease farrow-to-finish operation (MD). The pigs were of similar genetic background and were housed under similar conditions until slaughter. During the grower phase, MD had the highest average daily gain (ADG) (P < 0.05), while it was similar for SEW and Conventional (P > 0.10). Feed to gain (F:G) was similar for MD and SEW (P > 0.10), and poorest for Conventional (P < 0.05). During the finisher phase, SEW had the lowest ADG, and the poorest F:G (P < 0.05); these parameters were similar for Conventional and MD (P > 0.10). Over the entire G-F period, performance of MD pigs was better than that of Conventional and SEW pigs. Performance depressions appeared related to the degree of anterior-ventral lung atelectasis, antibody titers to porcine reproductive and respiratory syndrome (PRRS) virus and Mycoplasma hyopneumoniae, thymus size, plasma insulin-like growth factor levels, stomach ulceration, nasal turbinate damage, and microbial burden. The current study shows that the growth depressing effects of exposure to infectious organisms can be substantial in G-F pigs. Key words: Pigs, growth performance, disease, cytokines
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".