Use of Axial X-Ray Microcomputed Tomography to Assess Three-Dimensional Trabecular Microarchitecture and Bone Mineral Density in Single Comb White Leghorn Hens
Bibliographic record
Abstract
Axial x-ray microcomputed tomography is a cost-effective technique with the potential to assess bone mineral density (mg/cc) in both cortical and cancellous bone in Single Comb White Leghorn hens. The technique requires little sample preparation and involves relatively simple data processing. The system described in this research is based on compact fan-beam type tomography, using a tungsten-anode x-ray tube with a relatively small focal spot (approximately 5 microm), coupled with a high-resolution x-ray detector system (approximately 10 microm). To produce a real 3-D data set using microcomputed tomography, x-ray projection views were acquired at 720 equally spaced angular positions (0.5 degrees) around the object of interest. These groups of views were then used to reconstruct a computed tomography image. A test grid with orthogonal test lines was used to calculate bone volume and bone surface. From these calculations, parallel plate equations were used to derive trabecular architectural parameters such as average trabecular plate thickness and average trabecular plate separation. Three-dimensional microarchitecture was evaluated using specialized stereological analysis software. Significant relationships between apparent bone mineral density (mg/cc) and 3-D structure were observed in femoral specimens from 66-wk-old Single Comb White Leghorn hens.
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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 | 0.000 | 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 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".