Effects of dietary levels of 6-phytase on performance, nutrient digestibility, and tibia ash of broiler chickens fed corn–soybean meal-based complex diet
Bibliographic record
Abstract
This study evaluates phytase’s impact on broiler performance. Two thousand Ross 308 broilers were randomly assigned to 5 treatments based on body weight, with 40 birds per replicate pen and 10 replicate pens per diet. The 39-day experiment followed a four-phase feeding: starter 1 (days 0–4), starter 2 (5–11), grower (12–25), and finisher (26–39). The five treatments include basal diet 0 FTU/kg phytase (CON), CON + 500 FTU/kg (500 FTU/kg), CON + 750 FTU/kg (750 FTU/kg), CON + 1000 FTU/kg (1000 FTU/kg), CON + 1500 FTU/kg (1500 FTU/kg). The body weight increased ( p < 0.05) from starter 2 to finisher, and overall phase in the phytase-supplemented diets. Feed conversion ratio and mortality rate were lower ( p < 0.05) in the starter 2 and starter 1, respectively, in the phytase-supplemented diets. On day 24, the retention of phosphorous increased ( p < 0.05) when the supplementary level of phytase increased from 500 to 1500 FTU/kg. Furthermore, on day 39, the digestibility of dry matter, calcium, and phosphorus increased ( p < 0.05) as the level of phytase increased. The level of tibia ash and myo-inositol increased ( p < 0.05) in the phytase-supplemented diets. We concluded that increasing phytase doses from 500 to 1500 FTU/kg improved broiler performance over CON.
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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".