Bibliographic record
Abstract
There is no established method for assessing animal welfare, but various frameworks have been suggested (Table 1). Their application requires knowledge of animal health and production, and species-typical behavior. Knowledge of the latter may come from studies of the species’ lifestyle in the wild (4). Research is being done to develop practical methods of assessing welfare. One approach used the opinions of 35 experts, including veterinarians, to devise a list of questions for assessment of the welfare of poultry, cattle, and pigs during a half-day herd or flock visit (5). In another approach, the welfare of pregnant sows in 15 different housing systems was predicted, by using the available scientific data in a computer model (6,7). There is a large but incomplete body of data on the welfare of farm and laboratory animals, but fewer data are available on companion animals. However, the welfare or “quality of life” of companion animals is now being examined by veterinarians (8–10), and research is being done on this topic in Canada (10) and Denmark (Stine Christiansen, personal communication, 2003).
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 | 0.045 | 0.076 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.002 | 0.008 |
| Scholarly communication | 0.008 | 0.019 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| 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".