A training programme to ensure high repeatability of injury scoring of dairy cows
Bibliographic record
Abstract
Abstract Obtaining reliable welfare outcome measures from commercial farms can be challenging. We developed a training programme to train observers to score injuries of the tarsal joint, carpal joint and neck on dairy cows as part of an on-farm study. Twelve trainees were trained using protocols and photographs in a classroom session and on-farm visits. Continued repeatability checking was carried out during a refresher and mid-way assessment. Two trainers were used as the reference standard to which all trainees were compared. The study demonstrated that methods of scoring tarsal joint, carpal joint and neck injury can be learned by trainees from different backgrounds and high repeatability can be achieved and maintained at a very large regional or national level. Successful learning of injury scoring is dependent on protocols with strong definitions and photographs as well as repetitive training sessions. Additionally, continued repeatability checks are essential to ensure the reference standard continues to be met. This training programme can be used as a model to successfully train on-farm assessors.
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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 | 0.006 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".