Influence of dietary methionine to methionine plus cysteine ratios on nitrogen retention in gilts fed purified diets between 40 and 80 kg live body weight
Bibliographic record
Abstract
The relationship between the ratio of available methionine (MET) to methionine plus cysteine (TSAA) intake and wholebody protein deposition (PD) was established using the nitrogen (N) balance technique in gilts between 40 and 80 kg live body weight (BW), according to a repeated 5 × 5 Latin square design. Pigs were fed casein- and cornstarch-based diets that supplied equal moles of TSAA supporting a PD of approximately 80% of the gilts’ PD potential. On a weight basis, the target ratios of MET to TSAA were 42, 47, 52, 57 and 67% for the five experimental diets, respectively. This calculated to 37, 42, 47, 52 and 62.5% on a molar basis. Total N excretion (urine plus feces) was reduced (linear; P < 0.001) and PD was increased (linear; P < 0.001) when the available MET to TSAA ratio was increased to 52%; these values did not change (P > 0.05) when the MET to TSAA ratio was further increased. A number of statistical models were fitted to the data to establish the best fit of the model parameters. The greatest proportion of the variation (R2 = 0.990) was explained with an asymptotic model; based on this model the optimum available MET to TSAA ratio (supporting 90% of the asymptotic value for PD) was 55% on a weight basis or 50.5% on a molar basis. The results indicate that the minimum contribution of available MET to TSAA requirements of growing pigs is higher than the value currently suggested by the National Research Council (46 to 48% on a weight basis). Key words: Methionine, cysteine, pigs, nitrogen balance, protein, deposition
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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".