PSII-4 How to define good mothering ability using computer vision of free farrowing sows?
Bibliographic record
Abstract
Abstract In modern swine production, most of the sows are housed in a cage system in the farrowing unit. Although this system is intended to reduce preweaning mortality, the welfare of the sow might be compromised as it greatly restricts their natural behavior. Free farrowing systems allow sows to move around in the farrowing pen, which might stimulate their nesting behavior, shorten the farrowing duration and improve locomotion score, therefore, optimizing the longevity of the sows. However, the risk of increased preweaning mortality in free farrowing systems is greater compared with cage systems. Previous studies suggested that sow behavior, also known as mothering ability, can influence preweaning mortality as an increased level of posture changes could lead to increased mortality. With grunts, a sow could initiate the suckling of the piglets but also terminate it by hiding her udder from the piglets. However, there is conflicting evidence on what characteristics of maternal ability are correlated with piglet mortality, and most studies relied on limited manual observations, which might be biased. Using computer vision algorithms, the behavior and the movement pattern of a large number of sows could be monitored at a high density and on a continuous basis. Hence, this study intended to investigate the mothering ability of sows in free farrowing systems based on large quantities and unbiased observations using computer vision. Forty cameras were installed above free farrowing pens to observe the behavior of the sow. The dataset is continuously growing and will add data of 8 sows every week, totaling 416 sows on a yearly basis. The performance of the sows and their piglets are recorded, including body weight (BW), backfat, total number born, number weaned, birth weight, and weaning weight. Computer vision algorithms (YOLO) will be trained to estimate the contour and posture of the sow and the position of the piglets. Preliminary results based on a small subset of animals showed that the algorithm is able to detect the sows and their postures accurately, and it is now being extended to detect piglet locations. This will allow us to find beneficial behavior of sow and piglets, estimate genetic parameters for potential implementation in breeding programs, and eventually improve sow performance in a welfare-friendly free farrowing system.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".