Prediction of the amino acid digestibility of legume seeds in growing pigs: a meta-analysis approach
Bibliographic record
Abstract
On pig farms, a high proportion of the cost of production comes from feed costs. However, the use of alternative ingredients such as legume seeds may help to reduce this cost. In fact, legume seeds are an important source of essential amino acids (EAA) and can therefore be an alternative to oilseed meals. However, the accurate use of these legume seeds requires a precise knowledge of the standardized ileal digestibility (SID) of EAA, which may vary depending on its botanical variety. A meta-analysis was performed on a database compiling data from 41 studies published between 1981 and 2013 and 178 dietary treatments. Models of prediction of the SID of EAA as well as the dietary concentration of digestible standardized EAA (dEAA) were obtained, based on the chemical composition of ingredients reported in the publications. The effect of the type of legume seeds (faba bean, lupin, pea and soya bean), surgical procedures (T-cannula, re-entrant cannulas, post valve T-cannulas and ileo-rectal anastomosis), and BW of pigs (BW⩽25 kg BW>25 kg) were also tested in each model. Results showed that dietary CP and crude fibre (CF) were, respectively, the best predictors of each EAA SID for faba bean, lupin and pea (R 2=0.42 to 0.89) and soya bean (R 2=0.32 to 0.77). For the dEAA content, the best prediction models included dietary CP and ADF for faba bean, lupin and pea and soya bean, respectively, with R 2 ranging from 0.66 to 0.98. Models developed in this study allow predicting the digestibility of EAA in these alternatives feedstuffs.
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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 | 0.019 | 0.016 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.012 | 0.049 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".