Feed additives for methane mitigation: Regulatory frameworks and scientific evidence requirements for the authorization of feed additives to mitigate ruminant methane emissions
Bibliographic record
Abstract
mitigation. Regulations cover various aspects, including ingredient safety, manufacturing practices, product labeling, and the establishment of permissible limits for certain substances to ensure their safe use in animal feed. Compliance with these regulations is mandatory, and they are enforced by regulatory agencies within each jurisdiction, aiming to protect animal health, promote food safety, and prevent misleading claims and unsafe practices. The assessment processes involve evaluating scientific evidence submitted by applicants, along with evaluations of quality control procedures, and record-keeping practices. The major difference in regulations is that each jurisdiction developed unique criteria to legally classify AMFA, making it challenging to satisfy all legal classifications with a single set of criteria for scientific evidence. However, numerous similarities and a universal reliance on the concept of intended use indicate consistency across all jurisdictions on the need for robust evidence for efficacy, safety, and product quality and documentation even if the type, size, duration, and location of the studies they require differ. Recommendations are made for both scientists and applicants, emphasizing the importance of designing, conducting, and reporting scientific evaluations transparently, using validated standards and methods, and communicating with regulatory bodies to ensure compliance with regulations and evidence requirements.
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 | 0.148 | 0.138 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.003 | 0.006 |
| Bibliometrics | 0.009 | 0.005 |
| Science and technology studies | 0.007 | 0.009 |
| Scholarly communication | 0.016 | 0.008 |
| Open science | 0.011 | 0.009 |
| Research integrity | 0.034 | 0.015 |
| Insufficient payload (model declined to judge) | 0.006 | 0.005 |
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".