Evidence map›Paper›PMID 32488058›Full record

ArticleScientific reports2020

Quantifying defence cascade responses as indicators of pig affect and welfare using computer vision methods.

Poppy Statham, Sion Hannuna, Samantha Jones, Neill Campbell, G Robert Colborne, William J Browne, Elizabeth S Paul, Michael Mendl

Open access · goldAbstract read
In one paragraph

Article in Scientific reports, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed, 1 pooled it
1.9field-weighted citation impact, top 14% of its field
1 · What the graph read from it

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

4 citing papers in PubMed, 1 synthesis or guideline pooled it, 18 citations in OpenAlex.

  1. Pooled it
  2. Article
  3. Article
  4. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors at 2 institutions in 2 countries.

Poppy StathamAnimal Welfare and Behaviour Group, Bristol Veterinary School, University of Bristol, Langford House, Langford, BS40 5DU, UK.
Sion HannunaDepartment of Computer Science, University of Bristol, Merchant Venturers Building, Woodland Road, Bristol, BS8 1UB, UK.
Samantha JonesAnimal Welfare and Behaviour Group, Bristol Veterinary School, University of Bristol, Langford House, Langford, BS40 5DU, UK.
Neill CampbellDepartment of Computer Science, University of Bristol, Merchant Venturers Building, Woodland Road, Bristol, BS8 1UB, UK.
G Robert ColborneSchool of Veterinary Science, Massey University, Palmerston North, 4410, New Zealand.
William J BrowneSchool of Education and Centre for Multilevel Modelling, University of Bristol, 35 Berkeley Square, Bristol, BS8 1JA, UK.
Elizabeth S PaulAnimal Welfare and Behaviour Group, Bristol Veterinary School, University of Bristol, Langford House, Langford, BS40 5DU, UK.
Michael MendlAnimal Welfare and Behaviour Group, Bristol Veterinary School, University of Bristol, Langford House, Langford, BS40 5DU, UK. mike.mendl@bris.ac.uk.
University of Bristol · GBMassey University · NZ

Funding

Biotechnology and Biological Sciences Research Council BB/I005641/1
6 · The paper itself

Abstract

Affective states are key determinants of animal welfare. Assessing such states under field conditions is thus an important goal in animal welfare science. The rapid Defence Cascade (DC) response (startle, freeze) to sudden unexpected stimuli is a potential indicator of animal affect; humans and rodents in negative affective states often show potentiated startle magnitude and freeze duration. To be a practical field welfare indicator, quick and easy measurement is necessary. Here we evaluate whether DC responses can be quantified in pigs using computer vision. 280 video clips of induced DC responses made by 12 pigs were analysed by eye to provide 'ground truth' measures of startle magnitude and freeze duration which were also estimated by (i) sparse feature tracking computer vision image analysis of 200 Hz video, (ii) load platform, (iii) Kinect depth camera, and (iv) Kinematic data. Image analysis data strongly predicted ground truth measures and were strongly positively correlated with these and all other estimates of DC responses. Characteristics of the DC-inducing stimulus, pig orientation relative to it, and 'relaxed-tense' pig behaviour prior to it moderated DC responses. Computer vision image analysis thus offers a practical approach to measuring pig DC responses, and potentially pig affect and welfare, under field conditions.

Indexed as

Animal WelfareAffectAnimalsBehavior, AnimalBiomechanical PhenomenaFemaleImage Interpretation, Computer-AssistedMaleReflex, StartleReproducibility of ResultsSwineVideo Recording

Identifiers

PMID32488058
PMCPMC7265448
OpenAlexW3023624549

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.