The Efficacy of Quantum Phytase in a Forty-Week Production Trial Using White Leghorn Laying Hens Fed Corn-Soybean Meal-Based Diets
Bibliographic record
Abstract
Microbial phytase is a prominent feed enzyme used in animal feeds, but there is relatively little information on its use in laying hen diets. In this experiment, an Escherichia coli 6-phytase (Quantum) was evaluated for its efficacy in a 40-wk laying hen production trial. A total of 1,080 White Leghorn hens (540 each of Shaver and Bovan strains) were fed mash corn-soybean meal-based diets containing 0.35% (positive control, PC), 0.25% (negative control, NC1), or 0.15% (NC2) nonphytate phosphorus (NPP). Six more diets were manufactured by supplementing the negative control diets with 200, 400, and 600 U/kg of exogenous phytase, resulting in a total of 9 treatments. Each dietary treatment x strain subclass was replicated 4 times with 5 adjoining cages per replicate (3 hens per cage) in a randomized complete block design. Production performance was measured from 21 to 61 wk of age. Only minor differences in production characteristics were found between the PC and NC1 treatments regardless of phytase addition, indicating that 0.25% NPP resulted in P intake that was at or above the hen's requirement. In contrast, the hens fed 0.15% NPP diet without phytase supplementation had significantly (P < 0.05) reduced total hen housed egg production and body weight at 61 wk of age in comparison to the PC treatment, whereas the incidence of soft-shelled, cracked, and broken eggs was increased significantly (P < 0.05) in hens fed the NC2 diet. Addition of phytase to the NC2 diet improved these production characteristics to levels equal or better than the PC diet. The results indicated that Quantum phytase was efficacious in corn-soybean meal-based diets fed to White Leghorn laying hens and can be used to reduce diet supplementation with inorganic phosphorus.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".