Development and implementation of a training program to ensure high repeatability of body condition scoring of dairy cows
Bibliographic record
Abstract
A body condition score (BCS) in dairy cattle is a subjective assessment of the proportion of body fat that she possesses and is a common measure used in animal welfare assessment. The objectives of our study were to develop and implement a training program to produce highly repeatable BCS by many assessors as part of a cross-Canada epidemiological study on dairy cow comfort and welfare. In preliminary studies, we established that without any proper standard operating procedures (SOP) to describe the practical steps of the process and good standard reference for each score, assessors provided with a BCS chart scored with each other only with substantial agreement within 0.5 points and moderate agreement on exact score (mean weighted kappa coefficient=0.79 and 0.46, respectively). Detailed SOP were developed to assess BCS in 4 locations on a dairy farm. Assessing BCS presented more challenges in some locations (when cows exited the milking parlor, when the assessor was located outside the freestall pen) than others (when cows were headlocked at the feed bunk, when assessor was located inside the freestall pen). Additionally, training material and a training procedure were developed to ensure that future assessors would achieve almost perfect repeatability with the trainer within 0.5 points (weighted kappa coefficient >0.80). Twelve trainees followed this training and their repeatability was assessed using photographs in classroom sessions and live observations on farm over a 1-wk period. Repeatability was maintained above target agreement at periodic checks over the 6 mo of on-farm data collection. Two trainers were used as a reference standard to which all trainees were compared. This study demonstrates that to obtain reliable measures, a training program must include validated procedures to help assessors cope with a variety of farm setups. Regular repeatability checks are essential to ensure that the reference standard is maintained over time and to secure high data quality. This method to develop a training program as well as the training program implemented 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 | 0.010 | 0.012 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".