Bibliographic record
Abstract
It would be safe to say that one of the next big animal welfare issues for swine will be dry sow housing. Many countries have already started to change the way sows are housed. Most noticeable is the ban on dry sow stalls in the UK. Canadian swine producers can not get caught dismissing this trend. There are organizations already at work to try and change the use of dry sow stalls. Although legislation is unlikely in Canada, pressure on large food suppliers by consumer groups or special interest groups is already happening. Group housing systems have taken hold in Canada and there are many systems that have shown success. This is no longer new technology, and Canadian producers should be looking at implementing some of the loose housing practices before it is imposed on them. By no means is a dry stall ban the answer. In fact, dry sow stalls help in the welfare of the animal being raised. A proactive approach, by producers, in finding an alternative is the best solution. If Canadian producers were to research, design and implement a new sow housing protocol that is good for the sow and the producer, which incorporates both stalls and loose housing, then consumer groups and special interest groups will have a more difficult task implementing their agenda.
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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.035 | 0.006 |
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".