View: List Claim type Research area Organism Taxon as written Decade Paper Method / resource observed original result positive measured Claim · model-written summary
Accuracy from prediction error variance was a useful indicator of genomic prediction accuracy.
accuracy of prediction e0 accuracy of genomic breeding values from the prediction error variance e0 Not reported Taxon
Verbatim
e0 “the accuracy of genomic breeding values from the prediction error variance derived from the mixed model equations is a useful indicator of the accuracy of prediction . ”
✓ located verbatim in the source · abstract, characters 1061–1227 Other claims from this paper (5) Method / resource observed original result measured Claim · model-written summary
SPASIBA had median spatial prediction error of 188.7 km.
median prediction error e0 Verbatim
e0 “Continuous spatial prediction with SPASIBA showed a median prediction error of 188.7 km ”
✓ located verbatim in the source · abstract, characters 1078–1165 e1 “Cedrela odorata (Spanish cedar)
✓ located verbatim in the source · abstract, characters 186–217 Other claims from this paper (4) Method / resource observed original result positive measured Claim · model-written summary
Multiple covariates can improve prediction accuracy or reduce normalized root mean square error .
prediction accuracy or reduce the normalized root mean square error e0 Not reported Comparator
Verbatim
e0 “incorporating multiple covariates can improve prediction accuracy or reduce the normalized root mean square error ”
✓ located verbatim in the source · abstract, characters 1301–1414 t0 “wheat ”
✓ located verbatim in the source · title, characters 90–95 Other claims from this paper (7) Method / resource observed original result positive measured Claim · model-written summary
Feature engineering of environmental covariates reduced genomic prediction error by 761.625% across predictors compared with inclusion without feature engineering.
prediction error e0 inclusion of the environmental covariates without feature engineering e0 Verbatim
e0 “feature engineering helps reduce prediction error regarding only the inclusion of the environmental covariates without feature engineering by 761.625% across predictors ”
✓ located verbatim in the source · abstract, characters 834–1002 t0 “plant genomic-enabled prediction ”
✓ located verbatim in the source · title, characters 57–89 Other claims from this paper (1) Method / resource observed original result measured Claim · model-written summary
The study confirmed bias variance tradeoff and adaptive prediction error results for STACK.
bias-variance tradeoff and adaptive prediction error e0 Not reported Comparator
Verbatim
e0 “Our study has confirmed the results of bias-variance tradeoff and adaptive prediction error for the ensemble-learning-based model STACK ”
✓ located verbatim in the source · abstract, characters 1129–1264 t0 “Bread Wheat ”
✓ located verbatim in the source · title, characters 85–96 Other claims from this paper (1) Method / resource observed original result positive measured Claim · model-written summary
Mixture models drastically reduced prediction error variance, CV and residual variance on real coffee data.
prediction error variance, CV, and residual variance e0 mixture mixed models e0 Verbatim
e0 “approximated prediction error variance, CV, and residual variance were drastically reduced using mixture mixed models ”
✓ located verbatim in the source · abstract, characters 890–1007 t0 “Coffee Bean Progenies ”
✓ located verbatim in the source · title, characters 62–83 Other claims from this paper (8) Method / resource observed original result positive measured Claim · model-written summary
polyGDBLUP advantage over diploid models increased with dominance degree for accuracy, bias, mean squared error and mean absolute error .
prediction accuracy, bias, mean squared error and mean absolute error e0 diploid models e0 Not reported Taxon
Verbatim
e0 “the greater the dominance degree, the more obvious the advantages of polyGDBLUP over the diploid models in terms of prediction accuracy, bias, mean squared error and mean absolute error . ”
✓ located verbatim in the source · abstract, characters 974–1160 Other claims from this paper (6) Method / resource observed original result positive simulated Claim · model-written summary
Xi had at least 50% lower mean squared prediction error than alpha with unequal marker spacing.
mean squared prediction error of xi e0 Verbatim
e0 “The mean squared prediction error of xi was at least 50% smaller than that of alpha for linkage maps with unequal distances between adjacent markers ”
✓ located verbatim in the source · abstract, characters 932–1080 e1 “maize ”
✓ located verbatim in the source · abstract, characters 1137–1142 Other claims from this paper (2) Method / resource observed original result positive measured Claim · model-written summary
Dual-genome prediction models improved heritability estimates, error variances and model accuracy for maize Fusarium ear rot.
heritability estimates, error variances, and model accuracy e0 Not reported Comparator
Verbatim
e0 “Dual genome prediction models improved heritability estimates, error variances, and model accuracy ”
✓ located verbatim in the source · abstract, characters 830–928 t0 “maize (Zea mays L.) ”
✓ located verbatim in the source · abstract, characters 625–644 Other claims from this paper (3) Method / resource observed original result positive significant (as stated) measured Claim · model-written summary
Genotype by environment effects significantly reduced prediction mean squared error .
mean squared error (MSE) e1 Not reported Comparator Value
Verbatim
e0 “in Brassica napus ( B. napus ) ”
✓ located verbatim in the source · abstract, characters 760–790 e1 “the G × E effects reduced the mean squared error (MSE) significantly ”
✓ located verbatim in the source · abstract, characters 816–884 Other claims from this paper (4) Lexical search (BM25). All words must match; if nothing does, any word. Organism names also match every spelling of the same organism. No semantic search yet.
Cedrela odorata
Triticum aestivum
Coffea
Zea mays_Brassica_napus_-_Habit.jpg?width=60){kind=small}
Brassica napus