Reliability of a beef cattle locomotion scoring system for use in clinical practice
Bibliographic record
Abstract
BACKGROUND: Locomotion (lameness) scoring has been used and studied in the dairy industry; however, to the authors' knowledge, there are no studies assessing the reliability of locomotion scoring systems when used with beef cattle. METHODS: A four-point scoring system was developed and beef cattle filmed walking on a firm surface. Eight veterinary researchers, eight clinicians and eight veterinary students were shown written descriptors of the scoring system and four video clips for training purposes, before being asked to score 40 video clips in a random order. Participants repeated this task 4 days later. RESULTS: The intra-observer agreement (the same person scoring on different days) was acceptable with weighted mean Kappa values of 0.84, 0.81 and 0.84 respectively for researchers, clinicians and students. The inter-observer agreement (different people scoring the same animal) was acceptable with weighted Gwet's Agreement Coefficient values of 0.70, 0.69 and 0.64 for researchers, clinicians and students. Most disagreement occurred over scores one (not lame but imperfect locomotion) and two (lame, but not severe). CONCLUSION: This scoring system has the potential to reliably score lameness in beef cattle and help facilitate lameness treatment and control; however, some disagreements will occur especially over scores one and two.
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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 | 0.050 | 0.101 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".