Implementing animal welfare assessments at farm and group level: introduction and overview
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
Over the past decade there has been huge growth in the implementation of animal welfare assessments and audits in a variety of animal applications around the world. These include quality assurance programmes for meat, milk and eggs, certification for specialty brands or food-labelling programmes, accreditation of zoos, laboratories and animal shelters, and proofs of compliance with animal welfare standards required by regulatory agencies. Assessing animal welfare in such practical settings poses challenges at many levels. The animal welfare measures chosen for an assessment must be valid, repeatable and robust, and the sampling techniques must provide accurate representations of the overall welfare status of large groups of animals. Animal welfare assessors can come from a range of backgrounds with varying skill levels and experience and need to receive adequate training to ensure reliability. Although automated measures for animal welfare assessments can reduce potential for assessor error and bias, save time and reduce costs they must be sufficiently validated.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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 it