Use of Apparent Transverse Quantitative Ultrasonography to Assess Skeletal Integrity in Layers
Bibliographic record
Abstract
Although there are several techniques currently available to assess skeletal integrity in live birds, few offer important features for application in a commercial setting, offering ease of use and moderate cost. Quantitative ultrasonography (QUS) is an established technique for diagnosis of osteoporosis in humans and horses that has potential application for layers. An OmniSense 700S quantitative ultrasonometer was evaluated for use with Single Comb White Leghorn hens. Humeral QUS values (m/s) were measured in a series of experiments using a total of 144 Shaver White hens. Significant correlations (P < 0.01) were observed among sequential QUS measurements taken on the same bird at 54, 60, and 66 wk of age. At the completion of the studies (66 wk of age), the left and right humeri were excised, cleaned, and rescanned. Postmortem QUS data from left and right humeri were related (R2 = 0.72, P < 0.0001), although future studies may need to consider both sides of the skeleton to account for asymmetry conditions. Ultrasound data collected from live hens at 66 wk of age correlated well with postmortem QUS data (R2 = 0.80, P < 0.0001). Quantitative ultrasonography did not correlate with humeral bone-breaking force measured postmortem. Bones from live hens, surrounded by tissue thicker than 4 mm, could not be read by the QUS probe.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".