Protein intake but not feed intake affects dietary energy for finishing pigs
Bibliographic record
Abstract
The effects of dietary protein and feeding levels on dietary metabolizable (ME) and net energy (NE) content were determined in 24 pigs, each offered two diets at 2.0 times the energetic maintenance requirement or for ad libitum intake between 55 and 95 kg body weight. Within feeding levels, pigs received, in random order, low-protein (LP; 11.2% CP, 0.61% lysine) or high-protein (HP; 20.2% CP, 0.61% lysine) diets of similar digestible energy content. Dietary NE was calculated from heat production based on 24-h indirect calorimetry following a 7-day N-balance period. Feed intake was greater for LP than HP when fed for ad libitum intake (p = 0.001). Protein level did not affect daily gain (p > 0.1) but HP improved gain: feed (p = 0.003). Dietary ME and NE were not significantly affected by feeding level but were decreased by high protein intake (p < 0.07). Reducing dietary protein reduced urinary energy losses and increased energy retention but did not affect heat production. The effect of dietary protein restriction was already evident on the ME level and carried over to a similar degree to the NE level because the utilization of ME was not affected by protein level. Dietary ME and NE decreased by 0.012 MJ/kg (p = 0.014) and 0.018 MJ/kg (p = 0.062), respectively, for each gram per day N intake. The results suggest that although there was an effect of protein level on NE, the greatest effect occurred at the level of ME. However, the prediction of both ME and NE may be improved by adopting energy values for dietary protein that changes with dietary protein 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".