Comparing the Canadian pork lean yields and grading indexes predicted from grading methods based on Destron and Hennessy probe measurements
Bibliographic record
Abstract
In Canada, actual grading methods based on Destron (DPG) and Hennessy (HGP) probe measurements were approved in 1994. This study was undertaken to verify if both grading methods predict similar lean yields and grading indexes in actual pork carcasses. Data from the following four databases were used, and included hot carcass weight, and backfat and muscle depths as measured by both probes: 1281 carcasses from the 1992 National Cutout, 495 and 76 carcasses from 1997 and 1998 Fédération des Producteurs de Porc du Québec studies respectively, and 266 from a 1999 Agriculture and Agri-Food Canada study. Probes were inserted alternatively at the Canadian grading site. Grading indexes were assigned from a 1999 official grid. For the four studied databases, the HGP-DPG lean yields were different from zero (P < 0.0001) with values of 0.33, 0.35, 0.36 and 0.18%, chronologically. The HGP-DPG grading indexes were also different from zero with values of 0.51 (P < 0.0001), 0.36 (P < 0.0001) and 0.50 (P < 0.0001), 0.21 (P < 0.09), respectively. The slope between lean yields and indexes were different from one, indicating that the underestimation of lean yields and indexes by the DPG method increased with carcass leanness. Key words: Pork, Hennessy, Destron, probes, lean yield, prediction
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".