ArticleArchives animal breeding2026
Semantic modelling of animal welfare explained - Part 2: The basis of welfare weighting and usage of scientific information.
Article in Archives animal breeding, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
1 citing paper in PubMed.
- Semantic modelling of animal welfare explained - Part 1: Calculating overall welfare scores for husbandry systems using the ANyWEL model framework.Archives animal breeding · 2026Article
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
A methodology called semantic modelling can be used to synthesise scientific information to assess farm animal welfare. Different housing systems consist of attributes that can each have an effect on the welfare of the animal. Examples include type of floor, water provision system, and lighting system. Each of those attributes has so-called attribute levels. So, for instance, a water provision system can be drinking nipples, bowls, or troughs. Statements from the scientific literature that report the welfare effects of certain attribute levels serve as the basis for the welfare assessment. A semantic model takes attribute levels describing a housing system as input and generates a weighted overall welfare score as output. Although the procedure used in semantic modelling to decompose scientific statements into welfare-relevant attributes has been applied to multiple contexts, the procedure of information extraction has not been described in sufficient detail to allow new modellers to easily understand the procedure. Hence, the reproducibility of semantic modelling may be at stake despite its potential for integrated animal welfare research. Therefore, the objective of this paper was to provide a set of formalised rules showing how scientific information can be decomposed and weighted using semantic-modelling principles. We describe how to read the scientific literature to select scientific statements and how to decompose them to extract the relevant information for building a semantic model. Statement decomposition entails that the original formulation of the scientific statement is transformed into an "if-then" rule using this format: "if attribute A's level L1 is compared to its level L2 then there is a ([large] [significant]) effect on welfare measure M, with M belonging to a specified category of measures used for weighting (a so-called weighting category, WCat)". The attribute levels are then weighted based on the incidence, duration, and intensity of the measured welfare effect, expressed by a so-called weighting-category level score (WCatLevSc). The guidelines presented in this paper could be an important step towards the transparent use of available scientific information for sustainable development supporting both human and animal welfare.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.