703 Evaluation of National Research Council method of estimating body protein-to-lipid ratio in growing pigs
Bibliographic record
Abstract
A study was conducted to evaluate the accuracy of the equation estimating body lipid–to–body protein ratio (BLBP) of growing pigs proposed in the NRC (2012) model. In this model, BLBP is set as initial condition for growing finishing pigs to predict body composition from BW and is described by the equation BLBP = (0.305 − 0.000875 × PDMax) × BW0.45, in which PDMax (g/d) is the maximum protein deposition potential of the animal. The assessment was done using body composition data of 57 growing barrows (28 ± 2 kg BW) of a terminal cross line. The body protein and lipid content was measured using dual energy absorptiometry. The mean protein deposition of barrows from 25 to 55 kg BW (n = 19) and from 70 to 100 kg BW (n = 20) from the same batch was used to extrapolate the representative PD curve of the barrows as described by the NRC (2012). The maximum value from the curve was used as PDMax (190 g) in the BLBP equation. All the animals were fed at or above recommended nutrient requirements. The residual error analysis for BLBP prediction by the NRC equation revealed a root mean squared prediction errors (RMSPE; as a percentage of observed mean) of 35%. The average BLBP predicted value was 0.62, whereas the average observed value was 0.94. Most of the errors (89%) were due to mean bias and some were due to slope bias (9%). These errors indicate that the NRC-proposed BLBP equation underestimates BLBP. When the default PDMax (145 g/d) for growing barrows was used for BLBP prediction, the RMSPE was reduced to 19%, with 60% of errors partitioned to mean bias and 33% to slope bias. It was therefore concluded that the BLBP equation used in the NRC 2012 model underestimates body lipid–to–body protein ratio in pigs with high genetic potential for protein deposition.
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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 | 0.007 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".