Meta-analysis of the amino acid digestibility of oilseed meal in growing pigs
Bibliographic record
Abstract
Oilseed meal is an important source of essential amino acids (EAA) for livestock production. It is the second most important ingredient in pig feed after grains. Optimal use of these ingredients requires precise knowledge of amino acid standardized ileal digestibility (SID), which may vary depending on several factors including botanical variety or processing treatments. A meta-analysis was performed in order to derive models for predicting the SID of soybean, cotton and rapeseed meal EAA, based on chemical composition data such as CP, total concentration of each EAA and fibre (crude fibre, ADF and NDF) content. A database of 47 references (224 experimental treatments) was built. A model incorporating processing method of the meals (e.g. cold pressed, expeller pressed, solvent extracted), experimental surgical procedure (T-cannula, re-entrant cannula, post valve T-cannula and ileo-rectal anastomosis) and pig growth stage (BW⩽ or ⩾25 kg) was tested. Results indicated that neither processing nor BW affected EAA SID. NDF was the best predictor of SID (R 2=0.944, 0.836, 0.779, 0.899 and 0.814, respectively, for Lys, Met, Thr, Trp and Val). The total EAA content was the best predictor of digestible content (g/kg diet) for each EAA (R 2=0.990, 0.985, 0.977, 0.985 and 0.978, respectively, for Lys, Met, Thr, Trp and Val). This study shows that routine chemical analyses may be used to predict EAA digestibility with satisfactory accuracy.
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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 | 0.016 | 0.016 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.035 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".