Estimating real time individual lysine and threonine requirements in precision-fed pigs
Bibliographic record
Abstract
This study aimed to estimate independently real-time lysine (Lys) and threonine (Thr) requirements in growing-finishing pigs. Ninety-five pigs were randomly assigned to treatments according to 2×5 factorial arrangement with 2 amino acids (AA; Lys, and Thr) and 5 levels of incorporation (60, 80, 100, 120 and 140% of the estimated requirements) as main factors. Lysine and Thr requirements were estimated daily in real time during a 21-day trial. Data was analyzed using LOESS regression and the surface response methodology. The point of maximum response was obtained through a canonical analysis of the adjusted response surface and was found to be saddle-shaped, possibly due to the variability within AA requirements among individual pigs and thus pointed for several possible optimal Thr/Lys combinations. In conclusion, the non-unique response of the regression model indicates that there is a large between-animal variation in protein deposition (PD) response to Lys and Thr supply.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 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 | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".