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Claim · model-written summary
The predictive models had similar predictive ability.
Not reportedTaxon
Verbatim
e0“different predictive models had similar predictive ability performance”
Title
Genomic Selection with Allele Dosage in Panicum maximum Jacq.
Letícia A de C. Lara; Mateus F Santos; Liana Jank; et al. G3 Genes Genomes Genetics 2019. doi:10.1534/g3.118.200986
Claim · model-written summary
Greater sophistication of predictive aphid models has yielded little improvement in predictive ability.
Verbatim
e0“the quest for improved predictive ability through increased model sophistication has proved largely fruitless”
Title
N. D. Barlow New Zealand Entomologist 1982. doi:10.1080/00779962.1982.9722392
Claim · model-written summary
PLSR had the weakest predictive ability.
Not reportedComparatorValue
Verbatim
e0“PLSR showed the weakest predictive ability”
Title
Hyperspectral reflectance and machine learning to monitor legume feed quality
Chinmayi H. K.; K. Colton Flynn; Gurjinder S. Baath; et al. Agronomy Journal 2026. doi:10.1002/agj2.70386
Claim · model-written summary
Genomic breeding values had high predictive ability.
Not reportedTaxonComparatorValue
Verbatim
e0“Genomic breeding values (GEBV) showed high predictive ability”
Title
The Genetic Basis of Resistance to Late Blight in Tetraploid Potatoes
Hélène Romé; Merethe Bagge; Just Jensen Crop Breeding Genetics and Genomics 2026. doi:10.20900/cbgg20260001
Claim · model-written summary
Modeling gene interactions further improved predictive ability.
Oryza sativat0Not reportedComparatorValue
Verbatim
e0“Modeling gene interaction effects can further improve predictive ability”
t0“rice breeding population”
Title
Incorporating Gene Annotation into Genomic Prediction of Complex Phenotypes
Ning Gao; Johannes W R Martini; Zhe Zhang; et al. Genetics 2017. doi:10.1534/genetics.117.300198
Claim · model-written summary
GBLUP had the best overall predictive performance, with predictive ability 0.48.
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Eucalyptust0Not reportedComparator
Verbatim
e0“GBLUP exhibited the best overall performance, with a predictive ability of 0.48 and an R2 of 0.21”
t0“Eucalyptus spp.”
Title
Genomic Prediction in a Self-Fertilized Progenies of Eucalyptus spp.
Guilherme Ferreira Melchert; Filipe Manoel Ferreira; Fabiana Rezende Muniz; et al. Plants 2025. doi:10.3390/plants14101422
Claim · model-written summary
Genomic selection had moderate predictive ability.
Not reportedComparator
Verbatim
e0“there was moderate genomic predictive ability”
t0“Squash”
Title
Lauren J. Brzozowski; Michael Mazourek Agronomy 2020. doi:10.3390/agronomy10060847
Claim · model-written summary
Suitable preprocessing settings improved phenomic predictive ability.
Not reportedComparatorValue
Verbatim
e0“Our results showed that the predictive ability can be improved with the right settings.”
Title
Phenomic Prediction Can Be Improved by Optimization of NIRS Preprocessing
Vincent Braun; Xintian Zhu; Carina Meyenberg; et al. Plant Breeding 2025. doi:10.1111/pbr.13265
Claim · model-written summary
Some preprocessing combinations generally yielded very low predictive ability.
Not reportedComparatorValue
Verbatim
e0“some combinations generally result in a very low predictive ability”
Title
Phenomic Prediction Can Be Improved by Optimization of NIRS Preprocessing
Vincent Braun; Xintian Zhu; Carina Meyenberg; et al. Plant Breeding 2025. doi:10.1111/pbr.13265
Claim · model-written summary
Validation tests confirmed the model’s predictive ability.
Not reportedComparatorValue
Verbatim
t0“table grapes”
e0“the validation tests confirmed its predictive ability”
Title
Water loss in table grapes: model development and validation under dynamic storage conditions
Ericsem PEREIRA; Renata Gonçalves Broco e SILVA; Wigberto Antonio SPAGNOL; et al. Food Science and Technology 2017. doi:10.1590/1678-457x.08817
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