Method: Body composition assessment of sows using dual-energy X-ray absorptiometry
Bibliographic record
Abstract
For about 30 years, the introduction of dual X-ray absorptiometry ( DXA ) scanners in swine research has enabled the non-invasive study of body composition kinetics in animals. So far, the use of DXA technology in swine was focused on piglets, growing pigs up to about 140 kg of BW, as well as carcasses. Due to their size and weight, measuring a sow’s body composition is beyond the technical limits of the device. Furthermore, the chemical composition derived from DXA values is based on equations developed for pigs weighing between 20 and 100 kg. The present aim was to focus on the sow to (1) present a standard operation procedure to obtain the body composition of sows by DXA, and (2) assess the ability of available equations to predict a sow’s chemical body composition. For (1), a study investigated the effect of the animal’s position on DXA body composition. A total of 58 DXA acquisitions of sows were obtained on the standard ventral position (front and back legs extended) and on the lateral position (on left flank with right legs placed inward and left legs placed outward). The predicted BW, lean tissue mass, fat tissue mass, bone mineral content, bone area, and bone mineral density of the standard ventral position from the obtained lateral position resulted in root mean square prediction errors expressed as a percentage of the observed mean value of 0.5, 1.9, 5.0, 2.7, 3.1 and 3.5%, respectively. For (2), 3 sows were scanned alive and then slaughtered to measure chemical composition, then, these results were compared with equations based on growing pig data. The chemical composition of the carcass was predicted more accurately than that of the empty body. Regarding minerals, the Ca and P contents of the empty body were overestimated (12 and 3% respectively), as with the Ca content of the carcass (6%), while the P content of the carcass was underestimated (5%). In conclusion, the proposed material and operation procedure enables the scanning of sows which exceed the maximal specification of a DXA device. Furthermore, before concluding the accuracy of the chemical body composition prediction equations based on DXA data for pigs weighing between 20 and 100 kg, additional data are required to determine their applicability to sows.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".