196 Effects of thermal efficiency index on growth performance and carcass quality in grow-finish pigs.
Bibliographic record
Abstract
Abstract The thermal efficiency Index (TEI) generated through infrared thermography is associated with the growth efficiency in pigs. It is hypothesized that pigs with high TEI are energetically less efficient and produce more radiant heat to the environment, whereas those with low TEI are more efficient with greater ability to conserve energy for growth and production while minimizing energy loss to the environment. This study evaluated the growth performance and carcass quality of grow-finish pigs selected based on low and high TEI categories. A total of 176 eight-week-old pigs were enrolled in a 16-week experiment, with 88 pigs per batch (replicated once over time) in a randomized complete block design. At nursery exit, pigs were scanned using an infrared camera to determine TEI (mean dorsal temperature/body weight0.75). In each replicate, 44 pigs with high TEI (“HIGH”: 4.29 ±0.39; body weight 15.62 ±1.43kg) and 44 with low TEI (“LOW”: 3.48 ±035; body weight 20.28 ±1.82kg) were selected and housed at 11 pigs/pen. The study was partitioned into the grower (8-12 weeks of age), Finisher1 (12-16 weeks) and Finisher2 (16-20 weeks) phases. TEI and body weight were recorded at the end of each phase to determine average daily gain (ADG). Individual feed intake was recorded daily using automated feeders (IVOG pro) to determine average daily feed intake (ADFI) and Gain: Feed (G: F). Pigs were marketed at 20 weeks of age and carcass grading data were obtained. Mixed model was used for data analysis with TEI category as main effect and replication as random effect. Spearman’s Rho correlation was used to explore the relationship between TEI and feed efficiency. In the grower phase, LOW pigs tended to gain more weight (p=0.06), but there was no difference in ADFI or G:F. During Finisher1, LOW pigs consumed more feed (p=0.02) and had greater ADG (p=0.01) with no difference in G:F. During Finisher2, there was no difference in ADFI, ADG or G:F between TEI categories. The overall ADG throughout grower-finisher stage was greater for LOW than HIGH pigs (p=0.01). LOW pigs had greater net weight at slaughter (p< 0.01), and fat content (p=0.03). There was no effect of TEI on loin depth. The lean yield percentage tended to be lower in LOW than HIGH pigs (p=0.05). The results of this study showed a decreasing correlation between TEI and G:F overtime from 12-20 weeks of age. At week 12, there was a moderate positive correlation (r= 0.38, p< 0.01). By week 16, the correlation weakened (r=0.25, p< 0.01). At week 20, the correlation was further reduced and not significant (r=0.15, p=0.06). In conclusion, this study reveals that TEI can be used to identify pigs with better performance in terms of weight gain and carcass yield.
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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".