Guanidinoacetic acid supplementation improves reproductive performance of gilts during gestation and lactation
Bibliographic record
Abstract
ABSTRACT: This study evaluated dietary supplementation of guanidinoacetic acid (GAA) during gestation and lactation of high-prolific gilts on reproductive performance, milk amino acid profile and suckling piglets’ performance. A total of 53 gilts were distributed among 2 dietary treatments (Control diet (CON) and a GAA diet, CON + 1,0 g/kg of GAA (GAA)) in a completely randomized experimental design during gestation phase with 27 and 26 replicates respectively. During the lactation phase, according to the previous treatments and the use or not of GAA in the diets, the gilts were distributed in a 2 x 2 factorial design. The number of gilts were randomized design in four treatments: A control diet during gestation and lactation (CON-CON); a control diet during gestation and GAA during lactation (CON-GAA); GAA during gestation and CON diet during lactation (GAA-CON); and GAA during gestation and lactation (GAA-GAA). Gilts per treatment consisted of 13, 14, 13, 13 respectively. GAA supplementation for gilts improved reproductive traits and tended to increase the total number of piglets born alive (P = 0.059). The use of GAA during lactation increased daily milk production throughout this phase (P = 0.01), and improved milk amino acid content on d 7 of lactation (P < 0.5). Consequently, GAA supplementation in gestation and lactation impacted on lactation performance in first parity gilts, improving daily litter and piglet weight gain (P = 0.05), as well as litter and piglet weaning weights (P = 0.05). In conclusion, the use of GAA improves reproductive performance.
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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 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".