Redefining lameness assessment: Constructing lameness hierarchy using crowd-sourced data
Bibliographic record
Abstract
• Our lameness hierarchy method ranks cows from the most sound to most lame. • This method showed high inter-observer reliability among experienced assessors. • Hierarchy created by crowd workers closely matched that from experienced assessors. • This method enables quick, precise labeling for lameness videos of dairy cows. • 5-level gait scoring system showed low intra- and inter-observer reliability. Lameness causes pain to dairy cows and economic losses to farmers, but can be difficult to detect and 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 using the Elo-rating method. In this pilot study using 30 cow videos, our proposed lameness hierarchy constructed from pairwise lameness assessments achieved high inter-observer reliability (intraclass correlation coefficient (ICC) = 0.81) among experienced assessors. In contrast, we found that the traditional, subjective gait scoring systems to be inconsistent, with intra- and inter-observer reliabilities of ICC = 0.62±0.09 and 0.44±0.02, respectively. 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). We created a subsampling algorithm, and found that recruiting just 8 crowd workers per video pair was sufficient to construct a reliable lameness hierarchy. This method also decreased the number of pairwise comparisons required by 61 %, relative to evaluating all possible comparisons between every pair of cows. We conclude that our proposed lameness hierarchy method, using easily accessible crowd workers to facilitate quick and accurate labeling of lameness videos, enables a reliable and granular evaluation of lameness. We suggest that this approach be used to create large training datasets suitable for developing robust lameness detection models.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".