Supplementation of a low-energy diet with recombinant ferritin from <i>Perinereis</i> sp. can be beneficial to finishing pigs
Bibliographic record
Abstract
A total of 90 finishing pigs [(Yorkshire × Landrace) × Duroc] with an average body weight (BW) of 50.02 ± 1.78 kg were used in a 10 wk experiment. The pigs were distributed into three dietary treatments replicated six times with five pigs (two barrows and three gilts) per pen. The treatment diets were a positive control (PC; high-energy diet), a negative control (NC; low-energy diet), and an NC + 0.05% ferritin diet (TRT1). The supplementation of ferritin in a low-energy diet tended (P = 0.06) to increase the BW at week 5 compared with pigs fed low-energy diets without ferritin. At week 5 and overall period, the gain-to-feed ratio of pigs fed high-energy diets was higher (P < 0.05) compared with pigs fed low-energy diets. The pigs receiving a ferritin-supplemented diet had a comparable growth performance to pigs fed high-energy diets. At week 10, fecal Lactobacilli counts of pigs fed high-energy diets were higher (P < 0.05) compared with pigs fed low-energy diets. The supplementation of low-energy diets with ferritin resulted in comparable growth performance to pigs fed high-energy diets and had no adverse effect on digestibility and fecal gas emissions. Thus, it seems beneficial to include ferritin in low-energy diets of finishing pigs.
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 | 0.000 | 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".