Meta-analysis of phosphorus balance data from growing pigs
Bibliographic record
Abstract
Many studies have highlighted concerns over current methods of determining endogenous P losses and P requirements in growing pigs. Therefore, a database containing observations on 350 pigs was assembled from various studies. Four functions for analyzing P balance data were considered: 1) a straight line, 2) a diminishing returns function (monomolecular), 3) a sigmoidal function with a fixed point of inflection (Gompertz), and 4) a sigmoidal function with a flexible point of inflection (Richards). The nonlinear functions were specifically reparameterized to assign biological meaning to the parameters. Meta-analysis of the data was conducted to estimate endogenous P excretion, maintenance requirement, and efficiency of utilization. Phosphorus retention was regressed against either available P intake or total P intake [all variables scaled by metabolic BW (BW(0.75))]. There was evidence of non-linearity in the data, and the monomolecular function provided the best fit to the data. The Richards equation did not fit the data well and appeared overparameterized. Estimates of endogenous P excretion of 14 and 17 mg/kg of BW(0.75) x d based on available and total P analysis, respectively, were predicted by the monomolecular equation, which were within the range reported in the literature. Maintenance requirement values of 15 mg of available P/kg of BW(0.75) x d and 37 mg of total P/kg of BW(0.75) x d were obtained, based on the monomolecular equation. Average efficiencies of conversion of dietary P to retained P were 65 and 36% for available and total P, respectively, with greater efficiency values calculated for low P intakes. Although the monomolecular equation fitted the data best, more observations at high P intakes/kg of BW(0.75) are required to determine conclusively whether P retention scaled by metabolic BW is linearly related to available or total P intake.
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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 | 0.020 | 0.031 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.023 |
| Bibliometrics | 0.006 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".