View: List Claim type Research area Organism Taxon as written Decade Paper Method / resource observed original result positive measured Claim · model-written summary
The spectral index GM2 at the jointing stage gave the highest accuracy for yield prediction , with an R2 of 0.822.
jointing stage e3 accuracy for yield prediction e3 spectral index GM2 e3 Verbatim
t0 “Rice Quality and Yield Prediction ”
✓ located verbatim in the source · title, characters 0–33 e3 “Notably, the spectral index GM2 during the jointing stage achieved the highest accuracy for yield prediction , with an R2 value of 0.822 ”
✓ located verbatim in the source · abstract, characters 1092–1227 Other claims from this paper (5) Method / resource observed original result measured Claim · model-written summary
The modified vegetation index was the most accurate index for LAI prediction .
accuracy for LAI prediction e0 R2CV = 0.33, RMSECV = 1.21 m2m−2 e0 Verbatim
e0 “the modified vegetation index had the highest accuracy for LAI prediction (R2CV = 0.33, RMSECV = 1.21 m2m−2). ”
✓ located verbatim in the source · abstract, characters 1744–1853 t0 “For est Leaf Area Index ”
✓ located verbatim in the source · title, characters 107–129 Other claims from this paper (4) Method / resource observed original result positive measured Claim · model-written summary
The logistic regression prediction map had good accuracy (Kappa index 0.65).
accuracy of the LR method for the prediction map e5 Verbatim
t0 “Prediction of Distribution of Prangos Uloptera DC. ”
✓ located verbatim in the source · title, characters 0–50 e5 “The accuracy of the LR method for the prediction map was good (Kappa index = 0.65) ”
✓ located verbatim in the source · abstract, characters 1349–1430 Other claims from this paper (4) Method / resource observed original result positive significant (as stated) measured Claim · model-written summary
The blue nitrogen index significantly improved prediction accuracy of rice canopy LNC compared with SR(533,565).
prediction accuracy of the canopy LNC e1 promoted significantly e1 Verbatim
e1 “made the prediction accuracy of the canopy LNC promoted significantly,compared with SR(533,565) ”
✓ located verbatim in the source · abstract, characters 870–965 t0 “rice ”
✓ located verbatim in the source · title, characters 91–95 Other claims from this paper (7) Method / resource observed original result positive measured Claim · model-written summary
Prediction accuracy was higher under HNW than LNR for all traits except yellow index .
except for the trait yellow index e0 Not reported Taxon
Verbatim
e0 “PA for all traits in HNW was higher than LNR for the same trait, except for the trait yellow index . ”
✓ located verbatim in the source · abstract, characters 1126–1225 Other claims from this paper (6) Method / resource observed original result measured Claim · model-written summary
Occurrence day of year of the vegetation index metrics had only a minor effect on yield prediction accuracy .
prediction accuracy e0 only a minor effect e0 Not reported Comparator Direction Value
Verbatim
e0 “day of year (DOY) in which the y occurred had only a minor effect on the prediction accuracy . ”
✓ located verbatim in the source · abstract, characters 1229–1321 e1 “prediction of maize yield ”
✓ located verbatim in the source · abstract, characters 833–858 Other claims from this paper (3) Recommendation author interpretation original result basis n/a Claim · model-written summary
NDVI, biomass and harvest index are the most suitable indices for improving the accuracy of wheat yield prediction .
accuracy of wheat yield prediction e3 Verbatim
e0 “The results obtained prove the potential of RF and XGBoost-based models to accurately predict wheat yield ”
✓ located verbatim in the source · abstract, characters 1289–1394 e3 “Normalized difference vegetation index (NDVI), biomass (BM), and harvest index (HI) are proposed as the most suitable indices for improving the accuracy of wheat yield prediction ”
✓ located verbatim in the source · abstract, characters 697–875 Other claims from this paper (6) Method / resource observed original result positive measured Claim · model-written summary
The integrated method had higher accuracy than vegetation index alone for aboveground dry matter prediction .
predicting aboveground dry matter e0 higher accuracy e0 vegetation index alone e0 Not reported Taxon
Verbatim
e0 “higher accuracy compared to using vegetation index alone for predicting aboveground dry matter (R2 = 0.92, RMSE = 1.56) ”
✓ located verbatim in the source · abstract, characters 1281–1400 Other claims from this paper (7) Method / resource observed original result measured Claim · model-written summary
Genomic selection on index values in univariate models gave for ward prediction accuracy of r = .21 to .44 for the 2013 validation set.
forward prediction accuracy e6 Verbatim
e6 “Genomic selection applied to index values in univariate models provided for ward prediction accuracy ranging between r = .21 to .44 for the 2013 validation set ”
✓ located verbatim in the source · abstract, characters 2150–2308 t0 “hard winter wheat (Triticum aestivum L.) ”
✓ located verbatim in the source · title, characters 9–49 Other claims from this paper (9) Method / resource observed original result positive measured Claim · model-written summary
The site index model had high prediction accuracy , with a root mean square error of about 1 m.
six major tree species t0 prediction accuracy e0 Verbatim
t0 “Model development to estimate site index values for six major tree species in North Korea ”
✓ located verbatim in the source · title, characters 0–89 e0 “The results show that the prediction accuracy was high, with a root mean square error of ~ 1 m ”
✓ located verbatim in the source · abstract, characters 822–916 Other claims from this paper (3) 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.
Oryza sativa
Zea mays
Triticum aestivum