Welfare Assessment in Tunisian Dairy Herds by Animal-Linked Parameters and Performance Efficiency
Bibliographic record
Abstract
Animal welfare considerations are becoming increasingly important for farming of animals, both in Tunisia and internationally. Practices which may have once been deemed acceptable are now being reassessed in the light of new knowledge and changing attitudes. And a clearly defined concept of welfare is needed for use in precise scientific measurements. If animal welfare is to be compared in different situations or evaluated in a specific situation, it must be assessed in an objective way. Likewise, welfare is a multidimensional concept and its assessment systems have been developed by researchers of the European project Welfare Quality®. These systems should include animal-based measures directly related to animal body condition, health aspects, injuries and behavior. In this context, a Tunisian study was carried out in 35 dairy farms to evaluate welfare quality of Tunisian Holstein population cows through some welfare indicators validated by the European project Welfare Quality®. The studied sample included 350 females (Holstein; 161 heifers and 189 cows). Avoidance distance (at the feeding rack and inside the stable), body condition, lameness, fertility, somatic cell count, and milk yield were assessed. The study showed that animals differ in their relationship with the stockholder, performance, and health state, early experience and temperament.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".