Occupational Health and Safety Regulations in the Dairy Industry
Bibliographic record
Abstract
The application of occupational health and safety (OHS) legislation in the dairy industry varies throughout the world. Generally there is no specific OHS legislation that applies to the dairy industry and mostly in countries the current OHS legislation applies to all workplaces with specific guidelines that apply to agricultural industries. The main difference between countries is in the application of OHS legislation specifically in relation to the size of the farms. In the USA, the OHS legislation, and therefore enforcement, does not, in most cases, apply to farms with less than 11 employees, whereas in other countries there is no minimum number of employees and in some cases such as the United Kingdom and Australia it covers all people who work on the farm. The other area of difference is in the use and publication of guidelines for the industry; some countries have a wide range of guidelines whereas other counties have few. Generally, this relates to the jurisdiction of the OHS legislation, which in several countries is not at a national level such as USA, Canada, and Australia. The main principal of OHS legislation is that all workplaces, including dairy farms, should be a safe and healthy place to work, and does not vary significantly between the countries reviewed even those with prescriptive legislation.
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 | 0.016 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".