Previous feeding level influences plateau heat production following a 24 h fast in growing pigs
Bibliographic record
Abstract
Factorial approaches to estimate energy requirements of growing pigs require estimation of maintenance energy requirements. Heat production (HP) during fasting (FHP) may provide an estimate of maintenance energy requirements. Six barrows were used to determine effects of feeding level on components of HP, including extrapolated plateau HP following a 24 h fast (FHPp). Based on a cross-over design, each pig was exposed to three feeding levels (1.55, 2.05 and 2.54 MJ metabolisable energy/kg body weight (BW)(0.60) per d) between 30 and 90 kg BW. Following a 14 d adaptation period, HP was estimated using indirect calorimetry on pigs housed individually. Dynamics of HP were recorded in pigs for 5 d during the fed state and during a subsequent 24 h fast. Metabolisable energy intake was partitioned between thermal effect of feeding (HPf), activity HP (HPa), FHPp and energy retention. Feeding level influenced (P<0.05) total HP during the fed state, HPf and activity-free FHPp (609, 644 and 729 (SE 31) kJ/kg BW(0.60) per d for low, medium and high ME intakes, respectively). The value of FHPp when expressed per kg BW(0.60) did not differ (P=0.34) between the three subsequent experimental periods. Feeding level did not (P=0.75) influence HPa. Regression of total HP during the fed state to zero metabolisable energy intake yielded a value of 489 (SE 69) kJ/kg BW(0.60) per d, which is a lower estimate of maintenance energy requirement than FHPp. Duration of adaptation of pigs to changes in feeding level and calculation methods should be considered when measuring or estimating FHPp, maintenance energy requirements and diet net energy content.
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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 | 0.000 | 0.000 |
| 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".