Bibliographic record
Abstract
The concept of ‘clean, green and ethical’ pig production is attracting growing attention around the world as producers, international traders of pig, and consumers are becoming increasingly aware of sustainable and socially acceptable animal production systems. While the terminology 'clean, green and ethical' does not lend itself readily to an all-encompassing definition, in pig production it broadly refers to a rearing system with scientifically sound and ethical practices that underpins the production of safe and wholesome pork. Both the physical environment and management of the environment, for example controlling nitrogen and phosphorus emissions, are relevant to ‘clean, green and ethical’ pig production. Outdoor housing systems used in Australia based on litter (straw, rice hulls) portray a more natural image of pig production than conventional indoor housing systems based on steel and concrete, even though many of the same practices (e.g. antibiotic injections) and problems (e.g. enteric diseases) occur in both systems. Animal welfare is also an important and often contentious issue for pig production; evidence of this includes the recent spate of announcements by large vertical integrators in the USA and Canada of plans to phase out sow stalls following intense pressure from animal rights lobbyists. Other factors to be considered in ‘clean, green and ethical’ pig production include Quality Assurance (QA), the ability to trace individual pigs to their property of origin, aspects of pig genetics, the use of feedstuffs free of genetic modification, no hormonal or antibiotic residues in pig meat, and specific-pathogen free herds. Issues associated with antibiotic use are integral to the basic premises of ‘clean, green and ethical’ pig production. Restrictions or outright bans on the use of antibiotic feed additives, as occurred in the European Union from January 1st 2006, reinforce the notion that antibiotics denigrate the notion of ‘clean, green and ethical’ production even though pig welfare is likely improved by their use. This paper reviews the practical approach that Australia has taken to the production of pigs and pig meat that will meet the discerning needs and demands of our current and future domestic and international markets. We have also used some international data and commentary to highlight certain aspects of our discussions.
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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.005 |
| 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; both teacher heads agree on what is shown here.
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".