Evaluation of a method estimating real-time individual lysine requirements in two lines of growing–finishing pigs
Bibliographic record
Abstract
The implementation of precision feeding in growing-finishing facilities requires accurate estimates of the animals' nutrient requirements. The objectives of the current study was to validate a method for estimating the real-time individual standardized ileal digestible (SID) lysine (Lys) requirements of growing-finishing pigs and the ability of this method to estimate the Lys requirements of pigs with different feed intake and growth patterns. Seventy-five pigs from a terminal cross and 72 pigs from a maternal cross were used in two 28-day experimental phases beginning at 25.8 (±2.5) and 73.3 (±5.2) kg BW, respectively. Treatments were randomly assigned to pigs within each experimental phase according to a 2×4 factorial design in which the two genetic lines and four dietary SID Lys levels (70%, 85%, 100% and 115% of the requirements estimated by the factorial method developed for precision feeding) were the main factors. Individual pigs' Lys requirements were estimated daily using a factorial approach based on their feed intake, BW and weight gain patterns. From 25 to 50 kg BW, this method slightly underestimated the pigs' SID Lys requirements, given that maximum protein deposition and weight gain were achieved at 115% of SID Lys requirements. However, the best gain-to-feed ratio (G : F) was obtained at a level of 85% or more of the estimated Lys requirement. From 70 to 100 kg, the method adequately estimated the pigs' individual requirements, given that maximum performance was achieved at 100% of Lys requirements. Terminal line pigs ate more (P=0.04) during the first experimental phase and tended to eat more (P=0.10) during the second phase than the maternal line pigs but both genetic lines had similar ADG and protein deposition rates during the two phases. The factorial method used in this study to estimate individual daily SID Lys requirements was able to accommodate the small genetic differences in feed intake, and it was concluded that this method can be used in precision feeding systems without adjustments. However, the method's ability to accommodate large genetic differences in feed intake and protein deposition patterns needs to be studied further.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".