62 Growth Performance and Mineral Status of 6 Kg Piglets Fed Reduced Levels of Dietary Calcium
Bibliographic record
Abstract
Abstract Imbalances between calcium (Ca) and phosphorus (P) impair growth performance and bone mineralization. However, reducing dietary limestone may change the buffering capacity of feed in early nursery piglets, which may help prevent post-weaning diarrhea. An experiment was conducted to evaluate the impact of reducing Ca post weaning compared to recommendations outlined by NRC (2012) or a low P diet. Dietary treatments consisted of 1) Low Ca (LCa; Phase 1, 0.51% Ca and 0.47% STTD P), 2) NRC recommendations (NRC; Phase 1, 0.85% Ca and 0.42% STTD P), and 3) a recommendation lower in phosphorus (LP; Phase 1, 0.65% Ca and 0.36% STTD P). Each diet was fed over 4 phases. Piglets (n = 953; 276/275 Fast X PIC 800 genetics) were blocked by room, sex, and initial BW (6 kg). Feed intake and pig weights were recorded weekly. At 12 d and 41 d, blood was collected and Dual-X ray (DXA) measurements were taken using 8 piglets per treatment. Fecal scores were evaluated during wk 4 and 5. Data were analyzed using MIXED procedure of SAS (SAS Inst. Inc., Cary, NC). There were no differences observed in mortality, overall growth performance, plasma Ca and P, and scour scores at either time point. At 12 d, the bone mineral content was reduced (P = 0.001) in LP pigs compared to LCa and NRC. At 41 d, bone mineral content was reduced in NRC in comparison to LCa while LP was intermediate (P = 0.001). Plasma parameters showed an increased magnesium (Mg) and Ca:Mg in LCa (P < 0.01) that can be related to bone resorption to face Ca hypocalcemia. Pigs were healthy with no enteric challenges, limiting the ability to observe a benefit in fecal scores. In conclusion, piglets can maintain growth and bone mineralization through a short-term limestone removal program.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".