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Record W2595529306

The validation of infrared thermography as a non-invasive tool to assess welfare in the horse (Equus caballus)

2011· article· en· W2595529306 on OpenAlexfundno aff
K.R. Burton, Carol Hall, Chris Wells, E. Ellen Billett

Bibliographic record

VenueNottingham Trent University's Institutional Repository (Nottingham Trent Repository) · 2011
Typearticle
Languageen
FieldVeterinary
TopicVeterinary Equine Medical Research
Canadian institutionsnot available
FundersNational Institute of Allergy and Infectious DiseasesNatural Environment Research CouncilWellcome TrustBiotechnology and Biological Sciences Research CouncilMedical Research CouncilScottish Natural HeritageAgriculture and Agri-Food CanadaFundação para a Ciência e a TecnologiaRoyal Veterinary CollegeDirectorate for Biological SciencesAgrisearchNational Institutes of HealthUniversity of BristolScottish Funding CouncilState Scholarships FoundationUniversity of GlasgowPfizerSchweizerische Akademie der Medizinischen WissenschaftenRoyal College of Veterinary Surgeons Charitable TrustUniversidade do PortoMinistry of Higher Education, MalaysiaDepartment for Environment, Food and Rural Affairs, UK GovernmentEconomic and Social Research CouncilAnimal Welfare FoundationEuropean CommissionUniversity of LeedsConsejo Nacional de Ciencia y Tecnología
KeywordsEquusThermographyHorseWelfareInfraredEnvironmental scienceEcologyPolitical scienceBiologyOpticsPhysicsLawPaleontology
DOInot available

Abstract

fetched live from OpenAlex

IntroductionThe relatively high saturated fatty acid (SFA) content in milk fat has raised criticism in the past since it is assumed to be associated with negative effects on human health, especially due to the presence of C12:0, C14:0 and C16:0.On the other hand, monounsaturated fatty acids (MUFA), such as c9 C18:1 (oleic; OA) and t11 C18:1 (vaccenic; VA) and polyunsaturated fatty acids (PUFA), such as n-3 and n-6 groups and c9t11 conjugated linoleic acid (CLA9) found in milk, have been linked to beneficial effects on human health (Haug et al. 2007).c9c12c15 C18:3 (ALN) and c9c12 C18:2 (LA) are the main n-3 and n-6 FA in milk respectively.Differences in milk fatty acid composition between different dairy management systems in UK, such as conventional, organic and low input, have been reported in other studies (Butler et al. 2008; Ellis et al. 2006).Grazing intake, silage and sward composition, forage:concentrate ratio and oilseed supplementation, all factors that widely vary between management systems, can be responsible for milk fat compositional differences (Dewhurst et al. 2006).In a previous study, a stronger effect on milk fatty acid composition was found when the differences on grazing intake between management systems were more extreme.The comparison between low input and conventional milk showed higher differences in milk fatty acid composition than when conventional milk was compared with organic (Butler et al. 2008).The aim of this study was to investigate and explain possible differences in milk fatty acid composition between farms under different management practices in the North East of England. Material and methodsMilk from the bulk tank of 20 farms in the North East of England, representing 4 different management systems, was collected every 8 weeks for 10 month period.Conventional systems are characterized by an average of 41% of their diet as concentrates, with conserved forage fed while animals are fully housed during winter but access to ryegrass pasture when conditions allowed in summer.In organic systems, cows graze in ryegrass/clover swards, usually between April and October, and are housed in winter, with an average 22% concentrate supplementation.On intensive farms, cows are milked 3 times per day, nutrition is consistent throughout the year and is based on silage, which in that case may also contain maize silage, and 49% concentrates.Farms that apply robotic milking were selected to have the same feeding practices as conventional farms in order to investigate the effect of milking procedure.Analysis of FA methyl esters was performed with a Gas Chromatography system (Shimadzu, GC-2014, Japan) using a Varian CP-SIL 88 fused silica capillary column (100m x 0.25mmID x 0.2µm film thickness).Peaks were identified using a 39 FAME and CLA isomer standards.Analysis of variance (ANOVA) using linear mixed effects model (LME) was used to analyze results in R statistical environment using "Management system" (conventional, organic, intensive, robotic) and "sampling month" (7 sampling months over one year) as fixed factors and farm number as random factor. ResultsCompared to conventional milk, organic milk showed significantly higher concentrations of C14:0, ALN, n-3 and n-3:n-6 ratio while milk from intensive farms showed higher milk n-6 concentrations.When organic milk was compared to milk from intensive farms, significantly higher concentrations of C14:0, VA, CLA9, ALN, n-3, higher n-3:n-6 ratio and significantly lower concentrations of LA were found.Robotic milking farms showed significantly higher milk concentrations of C12:0 and C14:0 and lower concentrations of CLA9 than conventional farms.Table 1 Relative proportions (%) of individual fatty acids and fatty acid groups in milk from organic, intensive and robotic farms compared with milk from conventional farms C12:0 C14:0 C16:0 Oleic VA CLA9 ALN LA SFA MUFA PUFA n-3 n-6 n-3:n-6 Organic +9.7 ab +8.4 a -6.5 -3.6 +15.2 a +6.7 a +76.3 a -12.0 b +0.8 -3.1 +7.0 a +69.4 a -11.0 b +84.0 a Intensive -0.9 b -0.8 b +2.2 -3.6 -21.6 b -21.6 b -18.8 b +28.7 a +1.4 -4.9 +9.8 a -18.2 b +28.2 a -35.1 c Robotic +16.8 a +8.1 a -1.1 -4.9 -20.0 b -25.2 b -1.7 b -14.2 b +2.7 -5.4 -13.2 b -2.9 b -12.3 b -16.9 b P-values

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.021

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.005
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.064
GPT teacher head0.298
Teacher spread0.234 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

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Citations2
Published2011
Admission routes1
Has abstractno

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