Weighing finishing pigs in motion: A walk-over scale for accurate weight estimation
Why this work is in the frame
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Bibliographic record
Abstract
Accurate and efficient weight estimation of pigs is crucial for optimizing production, ensuring animal welfare, and making informed decisions in swine farming. Despite technological advancements, obtaining precise individual pig weights remains challenging due to the dynamic nature of pig movement and the stress induced by traditional weighing methods, highlighting the need for innovative, non-invasive solutions. This study presents an automated walk-over scale system that leverages high-frequency load cell data, feature engineering, and machine learning techniques to estimate pig weights in motion, addressing the limitations of traditional weighing methods. The system’s effectiveness was validated in a real-world setting with 50 pigs across 944 walk-throughs, achieving a Root Mean Square Error (RMSE) of 2.87 kg and a Mean Absolute Percentage Error (MAPE) of 2.65% on a 20% pig-wise holdout validation set, demonstrating its potential as a practical solution for non-invasive, accurate weight monitoring in commercial pig farming operations. • Developed an automated walk-over scale for dynamic pig weight estimation. • Used high-frequency load cell data with machine learning for weight predictions. • Improved weight prediction with zero-acceleration weights and segmented features. • Achieved an RMSE of 2.87 kg and a MAPE of 2.65% in real-world farm trials. • Gradient Boosting Regressor was the most effective for predicting the weight of pigs.
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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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it