Impact of supplemental exogenous phytase and total calcium-to-total phosphorus ratios on growth performance, mineral utilization, and bone characteristics in broiler chickens fed soybean meal as the sole source of dietary phosphorus
Bibliographic record
Abstract
Calcium (Ca) most significantly influences the bioavailability of plant-derived phosphorus due to its formation of phytate–Ca chelates. A study was designed to examine the interactive effects of graded total Ca-to-total P ratios (Ca:P) and phytase on the standardized ileal digestibility (SID) of P and bone characteristics in diets formulated with soybean meal as the sole source of P. Male broiler chickens were fed six semi-purified diets prepared in a 2 × 3 factorial treatment arrangement with two levels of phytase (0 or 1000 FYT/kg of diet) and three levels of Ca:P (1.1, 2.0, or 2.8) for 72 h from day 18 to 21 post hatching. Birds were assigned to the experimental diets in a randomized complete block design with eight replicate cages per treatment. Increasing the Ca:P linearly decreased ( P < 0.05) the SID of P from 54% to 40%, whereas the supplementation of phytase improved the SID of P from 26% to 67%. The supplementation of phytase increased ( P < 0.05) the bone-breaking strength, whereas tibia ash was decreased ( P < 0.05). The results demonstrate that the adverse effect of increasing Ca:P on P utilization in a soybean meal-based diet is consistent with or without phytase supplementation.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".