Assessing the impact of Bos taurus x Bos indicus crossbreeding and postmortem technologies on the eating quality of loins from pasturefinished young bulls
Bibliographic record
Abstract
This experiment aimed to evaluate the effects of Brahman crossbreeding and postmortem technologies (electrical stimulation and vacuum aging) on eating quality of loins from pasture-finished bulls. Fifty yearling bulls representing five Brahman-influenced types (n = 10 each): Brahman (BRAH), F1-Angus (F1ANG), F1-Chianina (F1CHI), F1-Romosinuano (F1ROM), and F1-Simmental (F1SIM) were supplemented on pasture until reaching a desirable conformation at a suitable live weight of ca. 480 kg. All carcasses were classified as “Bullocks” according to U.S. standards. Carcass’s right sides were subjected to high-voltage electrical stimulation (ES) while the left sides were not stimulated (NOES). Longissimus lumborum (LL) steaks from ES and NOES carcasses were allotted either to the vacuum aging control treatment for 2 d (NOAGING) or 10 d (AGING). LL steaks were evaluated for Warner-Bratzler shear force (WBSF) and sensory traits by trained panelists. No differences in WBSF, juiciness, or flavor ratings were detected among breed types (P > 0.05). Sensory ratings for tenderness-related traits varied little with breed type (P < 0.05). Steaks from F1ANG received higher ratings for muscle fiber tenderness, overall tenderness, and amount of connective tissue, and differed (P < 0.05) from those of F1ROM and F1SIM which received the lowest ratings. Bullock loins were more responsive to ES+AGING in WBSF reduction and desirable tenderness ratings than other postmortem treatments (P < 0.05) by reaching a greater proportion (72%) of “tender” (WBSF < 40.1 N) steaks than AGING (48%), ES (36%), and NOES-NOAGING (24%) samples (P < 0.01). Tenderness of bullock loin steaks is marginally improved by crossbreeding; therefore, the application of ES+AGING is necessary to ensure a higher proportion of tenderloin steaks.
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 | 0.000 | 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".