Influence of dietary lysine and energy intakes on body protein deposition and lysine utilization in the growing pig.
Bibliographic record
Abstract
A serial slaughter study was conducted to determine the effects of true ileally digestible lysine (IDLys) intake and metabolizable energy intake (MEI) on whole-body protein deposition (PD) and dietary lysine utilization in pigs between 45 and 75 kg live weight (LW). Conventional N balances were determined at the start and end of the serial slaughter study. Semisynthetic diets based on casein and cornstarch provided protein-bound lysine to support protein depositions of approximately 70% (Lys70%, IDLys 11.1 g/d) or 90% (Lys90%, IDLys 13.2 g/d) of a determined maximum PD. During the serial slaughter study and at Lys70%, pigs were fed one of six levels of MEI ranging from 14.1 to 23.5 MJ/d; at Lys90%, pigs were fed one of seven levels of MEI ranging from 15.6 to 26.4 MJ/d. The serial slaughter study and N balances indicated that MEI and IDLys had independent effects on PD and lysine utilization. Lysine utilization (calculated as the fraction of absorbed available lysine, over and above maintenance lysine requirements, that was retained in body protein) and PD increased with increasing MEI until plateau values were reached. At the plateaus, PD was determined by lysine intake. When lysine intake determined PD, lysine utilization did not decline (P > 0.10) with increasing lysine intake. Based on the N balance study, there was no effect (P > 0.1) of LW on lysine utilization. The marginal efficiency of using absorbed available lysine for PD was 0.75 and was not affected by LW, MEI, or IDLys.
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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 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".