Using Data Collected for Production or Economic Purposes to Research Production Animal Welfare: An Epidemiological Approach
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.
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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 | 0.057 | 0.119 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.016 | 0.019 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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; 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".