262 Ability of Model that Predict Growing-Finishing Pigs Requirements to Predict Dietary Phosphorus Use in Replacement Gilts
Bibliographic record
Abstract
Abstract A modeling approach to predict dynamics of body phosphorus (P) and calcium (Ca) independent of soft-tissue growth in growing-finishing pigs has been developed (Lautrou et al., 2020). Considering that the feeding of replacement gilts differs from that of growing pigs, this study was investigated to evaluate the ability of this model and other available models for growing-finishing pigs (INRAE of Jondreville and Dourmad, 2005; NRC, 2012; CVB of Bikker et Block, 2017) to predict body P and Ca retention in gilts. Data from gilts fed in 2 phases (Growing (C): 55-95 kg, finishing: 95-140 kg) was used to evaluate the models. In the first phase, the gilts received a diet that provided 100% of the Ca and P requirements (C100; 2.1 g of digestible P) ad libitum. In the finishing phase, the gilts received a control diet (F100; 2.1 g of digestible P) or a rich diet providing 160% of the requirements (F160; 3.5 g of digestible P), and daily feed intake and the energy content was reduced for a gain of 700 g/d. Body composition data were used to evaluate the predictive ability of the model according to the mean square error of prediction (MSEP) and its subdivision into central tendency error (CTE), regression (RE) and disturbance (DE). At 95 kg, the proposed model adequately predicted body P (MSPE=2.26%, CTE=0.016%, RE=13.4% and DE=86.5%), while the other models had a higher CTE, thus indicating an underestimation. At 140 kg, accuracy of predictions decreased for all models, with the proposed model overestimating body P and the other models underestimating it. These results will help adapt existing models to replacement gilts.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.001 | 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".