Replication Data for: Redefining lameness assessment: Constructing lameness hierarchy using crowd-sourced data
Bibliographic record
Abstract
Lameness causes pain to dairy cows and economic losses to farmers, but can be difficult to routinely monitor. Despite numerous attempts to develop automatic detection methods, few have been successfully applied on farms. The development of reliable automated methods is likely restricted by the lack of large, labeled training datasets that capture the diversity in lameness cases within and among farms. Additionally, conventional gait scoring methods employed for annotating training videos are subjective and unreliable, adding noise to training data and thus hindering model performance. We propose a novel approach to lameness assessment in dairy cows, leveraging crowd-sourced data to construct a lameness hierarchy. In this pilot study using 30 cow videos, we evaluated the reliability of traditional gait scoring systems revealing their subjective and inconsistent nature, with intra- and interobserver reliabilities of ICC (intraclass correlation coefficient)=0.62 ± 0.09 and 0.44 ± 0.02, respectively. Conversely, our proposed lameness hierarchy constructed from pairwise lameness assessments, achieved a high interobserver reliability (ICC = 0.81) among experienced assessors. We also demonstrated feasibility for the pairwise assessment to be executed by untrained assessors (in this case crowd workers recruited via Amazon MTurk), evidenced by high agreement between hierarchies generated by crowd workers and experienced assessors (ICC = 0.85). Utilizing a 5-milestone subsampling algorithm, we found that recruiting just 8 crowd workers per video pair is sufficient to construct a reliable lameness hierarchy. This method also decreases the number of pairwise comparisons by 61%, relative to evaluating all possible comparisons between every pair of cows. The proposed lameness hierarchy method facilitates quick and accurate labeling of lameness videos and enables a more granular evaluation of lameness. We suggest that this approach can be used to create large training datasets suitable for developing reliable automatic lameness detection models.
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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 | 0.051 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.028 | 0.018 |
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".