Bibliographic record
Abstract
Nearly 1 billion pigs are in inventory on farms around the world. About half the pigs are found in China alone. The other regions with significant number of pigs include South East Asia, Japan, Brazil, Mexico, the United States, Canada and most of Europe. Among these geographical locations, we find three general types of production models: (1) modern, industrialized production systems [Modern]; (2) small-scale low-input, often-backyard, family operations [Backyard]; and (3) natural, organic, antibiotic-free, GMO-free systems [Natural]. Modern industrialized systems have been called factory farms by those that wish to criticize keeping pigs in buildings and pens. However, pigs were moved from mud lots to buildings to improve their welfare and their health in particular. Today, we may find very low pre-weaning mortality and high health among pigs in many industrialized systems. Likewise, some presumably high-welfare systems have high pre-weaning piglet mortality and significant numbers of sows with scratches and wounds compared with modern US industrialized systems (McGlone, 2006). We have also reported good welfare among sows in both indoor and outdoor production systems that are well managed (Johnson et al., 2001). One cannot support the idea that industrialized production systems are inherently bad for pig welfare. Likewise, one cannot assume that because sows and piglets are in more natural settings that their welfare is automatically good. Good and poor welfare are found both on industrialized and more on natural production systems.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".