Bibliographic record
Abstract
Construction of models that provide a realistic representation of metabolic utilization of energy in growing animals tend to be over-parameterized because data generated from individual metabolic studies are often sparse. In the Bayesian framework prior information can enter the data analysis through formal statements of probability because model parameters are random variables and hence, are assigned probability distribution (Gelman et al. 2004). The objective of the study was to introduce prior information in modelling metabolizable energy (ME) intake, protein (PD) and lipid deposition (LD) curves, resulting from a metabolism study on growing pigs of high genetic potential. A total of 17 crossbred pigs of three genders (barrows, boars and gilts) were used. Pigs were fed four diets based on barley, wheat and soybean meal supplemented with crystalline amino acids to meet Danish nutrient requirement standards. Nutrient balance and gas exchange were measured at approximately 25, 75, 120 and 150 kg body weight (BW) during which the pigs were in metabolic cages and confined to open circuit respiration chambers for the determination of energy partitioning. We assumed that measurements (ME intake, PD and LD) made on a given pig at a given time followed a multivariate normal distribution. Two different equation systems were adopted from Strathe et al. (2010), generating the expected values in the multivariate normal distribution. Non-informative prior distributions were assigned for all model parameters except parameters describing metabolic scaling (b) and partial efficiencies of PD and LD (kp and kf), ensuring parameter identifiability. Two sets of priors were derived from the literature in such a way that 95% of possible values were ranging from 0.40 to 0.80, 40% to 80% and 60% to 100% for b, kp and kf, respectively. Utilizing both sets of priors showed that the maintenance component was sensitive to the statement of prior belief and, hence, that the estimate of 0.91 MJkg0.60d1 (95% CI: 0.78; 1.09) should be interpreted with caution. It was shown that boars were superior in depositing protein as these had an estimated PDmax of 250 g d1 (95% CI: 237; 263) whereas barrows and gilts had a PDmax of 210 g d1 (95% CI: 198; 220). Furthermore, PDmax in boars was reached at 109 kg BW (95% CI: 93.6; 130), whereas barrows and gilts maximized PD at 82 kg BW (95% CI: 75.6; 89.5). The boars partitioned on average 56% more of the ME above maintenance towards PD at 25 kg BW than barrows and gilts, which progressively increased to about 10% at 150 kg BW. The Bayesian modelling framework can be used to refine the analysis of data resulting from metabolic studies on growing pigs, especially in instances when data are sparse and models over-parameterized.
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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 | 0.008 | 0.007 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.049 | 0.020 |
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".