Using Data Collected for Production or Economic Purposes to Research Production Animal Welfare: An Epidemiological Approach
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Epidemiologists use the analyses of large data sets collected for production or economic purposes to research production nonhuman animal welfare issues in the commercial setting. This approach is particularly useful if the welfare issue is rare or hard to reproduce. However, to ensure the information is accurate, it is essential to carefully validate these data. The study used economic data to research in-transit deaths of finishing pigs. The most appropriate model to fit the distribution of the outcome must be selected. A negative binomial model fit these data because the prevalence was low and most lots of pigs had no deaths. The study used hierarchical dummy variables to identify thresholds of temperature and humidity above which in-transit losses increased. Multiple variable modeling provides the foundation for the strength of epidemiological research. The model identifies the association between each factor and the outcome after controlling for the other factors in the model. The study evaluated confounding and interaction. Bias may be introduced when data are limited to one farm system, one abattoir, or one season. Census data enable us to understand the entire industry.
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.
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 | 0.009 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it