Analyses for Hormonal Substances in Food-producing Animals
Bibliographic record
Abstract
This unique and definitive reference on hormone abuse in food producing animals is for scientists, regulators and consumers. It contains the results of a meeting held in November 2006 to discuss the progress made by the Veterinary Medicines Directorate (VMD) supported programme on steroid abuse detection. When the "hormone ban" first arose in the EU, there were only 15 Member States. There are now 27 and some newer members lack the background knowledge of this issue. This book sets out to summarise the history and show the significant progress that has been, and continues to be, made in this area. It is the only comprehensive review of this subject available and contains input from leading researchers from around the world. The initial chapters provide valuable background information. For example, the chapter on toxicology and risk covers the controversies arising from the interpretation of the effects of artificial hormones in meat-producing animals. The book then goes on to deal with how the issue has been managed via national and international detection programmes. It finishes by covering the resulting cutting edge analytical science including current research using "omics"/ profiling for "natural hormones" and novel detection techniques such as IRMS. This book offers readers an insight into the risk management of an important food related issue and how current analytical analyses can assist evidence-based risk assessments. There is comprehensive coverage of all past and current issues relating to growth promoting hormone abuse in animals.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.023 | 0.017 |
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".