Use of dual-energy x-ray absorptiometry in non-ruminant nutrition research
Bibliographic record
Abstract
Precise body composition measurements are essential in animal nutrition studies because the impact of treatments is evaluated based on changes in body weight and composition. Various indirect techniques for animal compositional evaluation have been developed and evaluated for applicability in animal nutrition studies. A fast, accurate, minimally invasive method that requires little input is considered the ideal for providing information about the animal. Measurements obtained by dual-energy x-ray absorptiometry (DXA) are highly correlated with those obtained by chemical analysis and dissection. The algorithms of DXA software partition the six chemical components of the body (lipids, water, proteins, carbohydrates, non-bone mineral, and bone mineral) into three compartments (total body mineral content, fat mass, and lean mass). Questions have been raised about how this partitioning affects the precision of the DXA method. In addition, the relationship between the DXA measurements and dissected carcass tissues is nonrepresentational of the relationship between DXA and chemical analysis. Furthermore, since DXA devices and their software were developed primarily for human medicine, they may not be fully adequate for animal evaluation. Calibration is required to obtain true values. The DXA method has some advantages and disadvantages that should be identified and controlled before calibration. Nonetheless, DXA is a valuable tool that provides precise, repeatable body composition measurements of live monogastric animals and their carcasses.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".