Selection for feed efficiency improves production traits and digestibility and its relationship to the fecal microbiota in both Large White dam and sire lines
Bibliographic record
Abstract
This study evaluated production traits, apparent total tract digestibility (ATTD), and fecal microbiota composition in finishing boars ( n = 207) selected for feed efficiency (FE; low = LFE; high = HFE) based on estimated breeding value for feed conversion ratio (FCR) within a Large White dam and sire genetic lines. Also, the association between gut microbiota with production and digestibility traits was investigated. Regardless of the genetic line, HFE pigs presented low FCR ( P < 0.05), had thinner back fat ( P < 0.05) and had greater loin depth ( P < 0.05) than LFE pigs. Also, HFE pigs had a significantly higher ATTD for Ca and a tendency for a higher crude protein ( P = 0.06) and phosphorous ( P = 0.10) ATTD than LFE pigs. No significant differences in alpha- and beta-diversity measurements for fecal microbiota were seen between FE groups in each genetic line. The genera Lactobacillus and Prevotella were associated ( P < 0.01) with some growth performance (e.g., feed intake), carcass traits (e.g., backfat thickness), and nutrient digestibility (e.g., Ca). In conclusion, HFE pigs had favourable production traits and higher digestibility of key nutrients than LFE pigs; however, the bacterial genera were associated with phenotypic traits but not by genetic merit.
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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 | 0.001 | 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".