An update of the predicted lean yield equation for the Destron PG-100 optical grading probe
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Bibliographic record
Abstract
The objective was to update the equation used for prediction of pork carcass leanness with the Destron PG-100 optical grading probe. A recent cutout study (completed in 2020-2021) consisting of 337 pork carcasses was used for this research. An updated equation was generated using a calibration dataset (N = 188 carcasses) and prediction precision and prediction accuracy of the new equation was evaluated using a validation dataset (N = 149 carcasses). The updated equation was generated using forward stepwise multiple regression selection techniques in PROC REG of SAS, and the same parameters as the existing equation were used to fit the model. The updated Destron equation [89.16298 - (1.63023 × backfat thickness) - (0.42126 × muscle depth) + (0.01930 × backfat thickness2) + (0.00308 × muscle depth2) + (0.00369 × backfat thickness × muscle depth)] and the existing Destron equation [68.1863 - (0.7833 × backfat thickness) + (0.0689 × muscle depth) + (0.0080 × backfat thickness2) - (0.0002 × muscle depth2) + (0.0006 × backfat thickness × muscle depth)] were similar in their prediction precision for determination of carcass lean yield (LY), with the updated equation R2 = 0.75 and root mean square error (RMSE) = 1.97 and the existing equation R2 = 0.75 and RMSE = 1.94. However, when prediction accuracy was evaluated using the variance explained by predictive models based on cross-validation (VEcv) and Legates and McCabe's efficiency coefficient (E1), the updated equation (VEcv = 67.97%; E1 = 42.41%) was much more accurate compared with the existing equation (VEcv = -117.53%; E1 = -69.24%). Furthermore, when accuracy was evaluated by separating carcasses into 3% carcass LY groupings ranging from less than 50% LY to greater than 62% LY, the existing equation correctly estimated carcass LY 8.1% of the time, while the updated equation correctly estimated carcass LY 47.7% of the time. In an effort to further compare the abilities of the updated equation, comparisons were made with an advanced automated ultrasonic scanner (AutoFom III), which scans the entire carcass. The prediction precision of the AutoFom III was R2 = 0.83 and RMSE = 1.61, while the AutoFom III correctly estimated carcass LY 38.2% of the time and prediction accuracy calculations for the AutoFom III were VEcv = 44.37% and E1 = 21.34%). Overall, refinement of the Destron PG-100 predicted LY equation did not change prediction precision, but substantially improved prediction accuracy.
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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 | 0.000 |
| 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 it