Research Note: Impact of Eimeria on apparent retention of components and metabolizable energy in broiler chickens fed single or mixture of feed ingredients-based diets
Bibliographic record
Abstract
The effect of Eimeria on apparent retention (AR) of components and metabolizable energy corrected for nitrogen (AMEn) content in corn, wheat, soybean meal (SBM), and pork meal (PM) was investigated in broiler chickens. A total of 840 male d-old Ross 708 chicks were placed in 84 cages (10 birds/cage) and allocated either a nitrogen-free diet (NFD), or 1 of 6 test cornstarch-based semipurified diets: 1) corn, 2) wheat, 3) SBM, 4) PM, 5) corn, SBM, and PM (CSP) mixture, and 6) wheat, SBM, and PM (WSP) mixture (n = 12). Diets contained 0.3% titanium dioxide and nutrient digestibility was determined by difference method using NFD. On d 10, birds in half of replicates per diet were orally challenge with 1 mL of E. acervulina and E. maxima culture and the other half equal volume of saline. Excreta samples were collected from d 12 to 14. With exception of AR of Ca and P, there was no interaction (P > 0.05) between Eimeria and diet on AR of dry matter, crude fat (CF), crude protein and gross energy and AMEn of ingredients. Eimeria reduced AR of CF (P = 0.01) and had a tendency to reduce AR of DM (P = 0.09) and AMEn (P = 0.063) of ingredients. The data demonstrated exposure to Eimeria impacted nutrient retention and energy utilization irrespective to diet composition.
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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 | 0.001 |
| 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".