66 Strategies to Improve Phosphorus Utilization in Growing Pigs: Depletion-repletion Protocols
Bibliographic record
Abstract
Abstract To optimize the use of dietary P by pigs, 5 feeding strategies were studied in a 3-phase feeding trial on 240 pigs (initial bodyweight (BW) of 31 kg): 1) C-C-C providing 100% of digestible phosphorus (Pdig, 4.3 g/kg STTD) and calcium (Ca, 9.7 g/kg) requirement to maximize bone mineralization, 2) L-L-L 60% of the Pdig and Ca requirements of C-C-C, 3) Phyt-Phyt-Phyt (phosphate-free, with phytase, 750, 686, 390 FTU/kg), providing 60% of Pdig and Ca requirements in phase 1, then 100%, 4) and 5) C in phases 1 and 3, and 60% of the need for Pdig in phase 2, associated with 65% of the requirements for Ca (N) or 80% (H), namely C-N-C and C-H-C. The BW and bone mineral content (BMC) were measured at the beginning and end of each phase. The BMC gain (gBMC), average daily gain (ADG) and average daily feed intake (ADFI) were calculated by phase. In phase 1, ADG was lower in the Phyt group than the C group (1.05 vs 1.10 kg/d, P < 0.01) and the BMC of group C and gBMC were higher than those of the Phyt and B groups (P < 0.05). In phase 2, C-C and Phyt-Phyt groups had similar BMC due to higher gBMC in the Phyt-Phyt (27.1 vs 18.4 g/d, P < 0.01). At the end of phases 2 and 3, C-C-C, C-N-C and C-H-C groups had similar BMC. The Phyt and B groups showed an increased phosphorus-use efficiency during phases 1 and 2 (+20% vs C). Phosphorus retention was also higher in the C-N-C and C-H-C groups, during the depletion in phase 2 (+24% vs C, P< 0.05). These results showed the potential of a depletion-repletion strategy including free phosphate diet to reduce phosphorus intake and excretion without affecting final growth performance and bone mineralization because of increased minerals utilization efficacies.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".