Hierarchical clustering as a tool to develop a classification scheme for rabbit meat quality
Bibliographic record
Abstract
This study aimed to characterise the quality of meat from commercially-raised rabbits. Animals came from five different producers and were laughtered in three different plants under provincial or federal inspection jurisdiction. Animal behaviour evaluated by scan sampling prior to feed withdrawal (FW) and transport, as well as blood lactate concentration at exsanguination, did not raise concerns with respect to stress. Stomach pH was higher (P=0.047) when the FW time was short (≤13.5 h), at a mean value of 2.23. All pH values measured 1 h post-mortem from the Biceps femoris (BF) and almost all (97.6%) from the Longissimus lumborum (LL) were higher than 6. Values for ultimate pH measured 24 h postmortem(pHu) ranged from 5.80 to 6.83 and from 5.70 to 6.70 for BF and LL muscles, respectively. The maximum meat drip loss recorded was 2.6%, while cooking loss reached 30%. Meat lightness (L*) and colour intensity (C*) for the long FW times (≥23 h) were no different from those with short and intermediate (15.5 to 17.3 h) FW times. However, these colour parameters were higher for the short FW time class compared to the intermediate FW time class (P<0.02). A hierarchical cluster analysis based on pHu, cooking loss and lightness (L*) from 200 rabbit loins was performed. Of the four clusters created, clusters 1 and 2 had the best and second-best meat quality, respectively. Clusters 3 and 4 had the lowest meat quality and presented DFD-like (dark, firm and dry) characteristics. Meat did not exhibit PSE-like (pale, soft, exudative) characteristics, even for the slaughter lot with the minimum mean pHu. Of the eight slaughter lots evaluated, more than 50% of the meat from three of them fell into clusters 3 and 4; all three were in the intermediate FW time class. Overall, the quality of rabbit meat analysed was acceptable for commercial use, but rather variable. This suggests that there are factors within the value chain that are not yet fully controlled and require further investigation.
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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 | 0.007 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".