Impact of a constant current electrical stimulation (CCES) system and hormonal growth-promoting (HGP) implants on meat quality and palatability of finished steers
Bibliographic record
Abstract
This study evaluated the effects of a constant current electrical stimulation (CCES) system and hormonal growth-promoting (HGP) implants on the quality and palatability of the longissimus thoracis et lumborum (LTL) from yearling-finished steers. The experiment used a total of 46 Angus cross steers, which were either non-implanted (n = 20) or implanted with trenbolone acetate and estradiol benzoate (n = 26). The CCES was applied to one side of each carcass during the slaughter process, whereas the other side remained unstimulated. Regardless of the application of HGP implants, the CCES reduced pH at 3 and 72 h post-mortem and shear force at all ageing times (P < 0.05), improved colour at 72 h post-mortem and during the retail display (P < 0.05), increased initial and overall tenderness (P < 0.01), and decreased the amount of perceived connective tissue and the proportion of trained panelists detecting spongy texture (P < 0.05) compared to meat from unstimulated carcass sides. Although CCES increased meat purge losses and reduced moisture content (P < 0.05), this did not affect meat juiciness (P > 0.10). CCES interacted with HGP to prevent increase in drip loss (P > 0.10), increase frequency of panelists detecting bloody/serumy flavour and typical texture, and reduce the proportion of panelists detecting rubbery texture in meat (P < 0.05). Regardless of stimulation treatment, meat from implanted animals had a more pronounced pH decline at 72 h post-mortem (P < 0.05) and a higher proportion of panelists finding no off-flavours (P < 0.05) or bloody/serumy flavour (P < 0.01) than non-implanted cattle. The CCES system tested in this study improved LTL quality and palatability of heavier beef carcasses.
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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 | 0.000 | 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) | 0.001 | 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".