PSIX-18 Prediction of urinary P excretion as a tool to assess mineral status of growing pigs
Bibliographic record
Abstract
Abstract Urinary losses of phosphorus (P) and calcium (Ca) are indicators of absorbed compared to amount needed for tissue growth as well as their balance for bone deposition. They can thus be a good indicator of the mineral status. This hypothesis has been validated through meta-analysis using a database of P and Ca retention in growing pigs including 43 publications published between 1969 and 2018 for a total of 51 experiments and 251 dietary treatments. P urinary excretion has been simulated using a multiple regression analysis with the proc MIXED (Minitab® 19) with trial effect as random. Average body weight of the pigs was 32kg ±18kg. Urinary P losses (urineP) was influenced by apparent digestible P (digP, recalculated for each treatment; g/kg), total Ca (Ca, g/kg), and microbial phytase (PhytM, FTU/kg). The model created explains 90% of urineP (R2 = 91%). UrineP was increased with digP from about 2 g/kg of digP (digP, P = 0.02, digP x digP, P < 0.001), before this point it was very low. Increasing dietary Ca decreased urineP linearly when exceeding the 2 g/kg of digP (Ca x digP, P < 0.001); P can then be fixed into bone as hydroxyapatite. The contribution of P is thus to manage in conjunction with dietary Ca. PhytM effect depends of both Ca and P (PhytM x Ca x digP; P = 0.03) showing that phytM addition reduces urineP depending of digP and Ca. As an example, in 6 g Ca/kg diet, 500 FTU/kg addition reduce urineP by 28% in 2 g digP/kg and by only 4% in 4 g digP g/kg. This is due to a better retention of P into bone in low digP. Next step will be urinary Ca modeling and then looking at meaning of their ratio in relation to mineral status to develop a practical tool to assess Ca and P status of pig.
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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 | 0.010 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.005 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".