Bibliographic record
Abstract
Abstract Over the last 50 years the veal industry has seen a number of changes, in particularly in production systems with the introduction and acceptance of grain‐fed and heavier calves and the progressive move from individual pens to group housing. Reasons for the changes are multi‐faceted of which two important players are the well‐being of the animal and the public perception of the industry. Regardless of the reasons for the changes, breeders strive to attain veal conforming to the rigorous standards reflecting consumer demands. Consequently a multitude of publications exists on production factors in veal farming. However, many of these reports stop at the ‘farm gate’, or more correctly, the slaughterhouse, where carcass characteristics in particular are assessed. Changes in production systems generally aim to improve feed efficiency and weight gains, but often overlook meat quality aspects which ultimately dictate financial gains. This review aims to summarise the existing and available literature on factors affecting the quality of veal meat. The topics covered include the effects of breed, sex, weight or age, diet composition and dietary treatments, environment and pre‐slaughter handling, and processing factors such as stunning, electrical stimulation, ageing and packaging. Copyright © 2006 Crown in the right of Canada. Published by John Wiley & Sons, Ltd.
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 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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".