Assessment of lameness in sows using gait, footprints, postural behaviour and foot lesion analysis
Bibliographic record
Abstract
Lameness in sows has an economic impact on pig production and is a major welfare concern. The aim of the present project was to develop methods to evaluate and quantify lameness in breeding sows. Five methods to study lameness were compared between themselves and with visual gait scoring used as a reference: footprint analysis, kinematics, accelerometers, lying-to-standing transition and foot lesion observation. Fifty sows of various parities and stages of gestation were selected using visual gait scoring and distributed into three groups: lame (L), mildly lame (ML) and non-lame (NL). They were then tested using each method. Kinematics showed that L sows had a lower walking speed than NL sows (L: 0.83 ± 0.04, NL: 0.96 ± 0.03 m/s; P < 0.05), a shorter stride length than ML sows (L: 93.0 ± 2.6, ML: 101.2 ± 1.5 cm; P < 0.05) and a longer stance time than ML and NL sows (L: 0.83 ± 0.03, ML: 0.70 ± 0.03, NL: 0.69 ± 0.02 s; P < 0.01). Accelerometer measurements revealed that L sows spent less time standing over a 24-h period (L: 6.3 ± 1.3, ML: 13.7 ± 2.4, NL: 14.5 ± 2.4%; P < 0.01), lay down earlier after feeding (L: 33.4 ± 4.6, ML: 41.7 ± 3.1, NL: 48.6 ± 2.9 min; P < 0.05) and tended to step more often during the hour following feeding (L: 10.1 ± 2.0, ML: 6.1 ± 0.5, NL: 5.4 ± 0.4 step/min standing; P = 0.06) than NL sows, with the ML sows having intermediate values. Visual observation of back posture showed that 64% of L sows had an arched back, compared with only 14% in NL sows (P = 0.02). Finally, footprint analysis and observation of lying-to-standing transition and foot lesions were not successful in detecting significant differences between L, ML and NL sows. In conclusion, several quantitative variables obtained from kinematics and accelerometers proved to be successful in identifying reliable indicators of lameness in sows. Further work is needed to relate these indicators with causes of lameness and to develop methods that can be implemented on the farm.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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 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".