PSVIII-5 Determination of true digestibility and the endogenous outputs of magnesium in corn for growing pigs by using the regression analysis technique
Bibliographic record
Abstract
Abstract Dietary magnesium (Mg) is essential to bone mineralization. Supplemental Mg is typically not considered in commercial swine diets by assuming high bioavailability of Mg from bulky feed ingredients such as corn grain. The objectives of this study were to determine true ileal and fecal digestibility and the endogenous losses of Mg associated with corn in growing pigs by the regression analysis technique. A total of 48 barrows, with an average initial body weight (BW) 32 kg, were randomly assigned to 6 grower pig diets and were fed close to ad libitum for 10 d, with 8-d adaptation and 2-d collection fecal and the terminal ileal digesta samples, according to a randomized complete block design. Six cornstarch-based diets, containing 6 levels of Mg at 0.22, 0.32, 0.38, 0.51, 0.71 and 0.79 g/kg dry matter intake (DMI) of diets, were formulated from corn. There were linear relationships (P < 0.05), expressed as g/kg DMI, between the ileal and fecal outputs of Mg and the total intake of dietary Mg, suggesting that true ileal and fecal Mg indigestibility values (94.8±12.5 vs. 89.2±17.7%); and the ileal and fecal endogenous Mg outputs (0.16±0.02 vs. 0.21±0.11, g/kg DMI of diets) associated with corn could be estimated by the regression analysis. Our results have shown that Mg associated with conventional corn grain was very poorly digested and the gastrointestinal endogenous fecal loss of Mg was significant in the grower pig. Thus, Mg bioavailability in feeds for pigs should be assessed and supplemental of Mg may be warranted in swine diet formulation.
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 | 0.001 | 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".