The investigation of ultrasound technology to measure breast muscle depth as a correlated trait to breast meat yield in turkey (Meleagris gallopavo)
Bibliographic record
Abstract
Ultrasound measurements of muscle depth were analyzed to determine if these traits could be used to increase the rate of genetic gain in breast meat yield (BMY). Two measurements of breast depth, one taken horizontally across both breast lobes and one parallel to the keel, were captured using ultrasound. Heritabilities of muscle depth traits ranged from 0.35 to 0.70. These values were greater than heritabilities of conformation scores, which ranged from 0.25 to 0.47 within sex and line. The ultrasound traits also showed strong genetic correlations to BMY, ranging from 0.43 to 0.75, indicating that selection, using ultrasound depth as a correlated information source, could result in improved BMY. Including each ultrasound trait in a linear regression model predicting BMY increased the proportion of variation explained by the models by 0.08 to 0.17, relative to using conformation score as the only in vivo estimate. Based on results from a simulated turkey breeding program with selection pressure only on BMY, the ultrasound measures could increase the accuracy of a selection index for BMY by 0.02 to 0.16. As a result, ultrasound technology has the potential to improve the rate of genetic gain in BMY in a breeding program.
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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 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".