Barn climate advice and pig tear staining: preliminary insights into an emerging welfare indicator
Bibliographic record
Abstract
Barn climate influences pig welfare and has been associated with tear staining (TS), a potentialwelfare indicator. This intervention study examined whether barn climate advice affects TS infinisher pigs and evaluated TS’s utility as a welfare indicator. Five Dutch Beter Leven onestar farmsparticipated in a larger study, with one production unit per farm equipped with a Slimme Stal sensor(Connecting Agri & Food) to monitor barn climate. A crossover design was used with two batchesper farm (advice vs. control), where farms were blocked and treatments randomized by batchstarting month. In the advice group, a climate advisor reviewed sensor data and recommendedadjustments. Up to five focal pens per unit were monitored for TS, lesions, tail biting severity, andlameness during two visits per batch—approximately two weeks after setup and one week beforeslaughter. TS (both eyes), tail biting, and lameness were scored on a 0–100 visual analog scale,and lesion num bers were classified according to the Welfare Quality® protocol. For each batch,changes in welfare indicators between visits were calculated. Climate data were summarized ashours exceeding thresholds for CO, NH, and a temperature-humidity index, while hourly outdoortemperature was averaged. Linear mixed-effect mod els (with treatment as a fixed effect and pensnested within farms as a random effect; base model) showed that treatment did not significantlyaffect TS changes (Left: P = 0.175; Right: P = 0.747), though the control group recorded more hoursof elevated CO (+266h; P = 0.0399) and NH (+439h; P < 0.001). Alternating individual climatevariables and changes in welfare indicators in the TS base models (left and right) did not reveal significantlinkages between TS and climate parameters. However, left-eye TS changes were positivelyassociated with increased ear lesions (β = 1.98, P < 0.01) and nearly so with increased lameness(β = 0.64, P = 0.083) and total lesions (β = 0.30, P = 0.067), while right-eye TS changes correlatedwith increased lameness (β = 0.77, P < 0.05) and nearly with front lesions (β = 0.66, P = 0.074).Although climate advice did not significantly impact TS, the linkage with other welfare indicatorshighlights the potential validity of TS as a welfare measurement. Im portantly, further research isneeded to validate TS as a robust welfare indicator, to delve deeper into eye-side differences, andto clarify the interplay between environmental and physiological stressors in pig production.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".