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“prediction error”

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Methods & resources 3688

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Method / resourceobservedoriginal resultpositivemeasured

Claim · model-written summary

Prediction error was frequently less than calibration error.

Taxon
AlfalfaMedicago sativat0
Outcome
prediction errore2
Direction
less thane2
Comparator
calibration errore2

Verbatim

t0

“Predictors of Alfalfa Forage Quality”

✓ located verbatim in the source · title, characters 0–36
e2

“the prediction error frequently was less than the calibration error”

✓ located verbatim in the source · abstract, characters 1110–1177

Title

Predictors of Alfalfa Forage Quality: Validation with Field Data

Matt A. Sanderson Crop Science 1992. doi:10.2135/cropsci1992.0011183x003200010049x

Other claims from this paper (5)
Method / resourceobservedoriginal resultnegativemeasured

Claim · model-written summary

EONR prediction error was about three times higher than yield prediction error across forecast times.

Outcome
prediction error of EONRe0
Direction
highere0
Value
about three timese0
Comparator
yield predictionse0

Verbatim

e0

“Across all forecast times, prediction error of EONR was about three times higher than yield predictions.”

✓ located verbatim in the source · abstract, characters 1789–1893
t0

“Corn”

✓ located verbatim in the source · title, characters 40–44

Title

A Systems Modeling Approach to Forecast Corn Economic Optimum Nitrogen Rate

Laila A. Puntel; John E. Sawyer; Daniel W. Barker; et al. Frontiers in Plant Science 2018. doi:10.3389/fpls.2018.00436

Other claims from this paper (6)
Method / resourceobservedoriginal resultmeasured

Claim · model-written summary

The root-mean-square error of prediction and the standard error of prediction were both 0.0026.

Taxon
foxtail milletSetaria italicae2
Outcome
the root-means-square error of prediction (RMSEP) and the standard error of prediction (SEP)e1
Value
0.0026e1

Verbatim

e1

“Accordingly, the root-means-square error of prediction (RMSEP) and the standard error of prediction (SEP) are both 0.0026”

✓ located verbatim in the source · abstract, characters 1026–1147
e2

“Seven varieties of foxtail millet were successfully identified”

✓ located verbatim in the source · abstract, characters 510–572

Title

Identification of Foxtail Millet Varieties Using Leaf Surface Spectral Information

Xiaoping Han; Wei Yang; Haiyan Song; et al. Sensors and Materials 2020. doi:10.18494/sam.2020.2718

Other claims from this paper (5)

Breeding, agronomy & horticulture 2408

Show all 2,408 →
Method / resourceobservedoriginal resultpositivemeasured

Claim · model-written summary

Prediction error was frequently less than calibration error.

Taxon
AlfalfaMedicago sativat0
Outcome
prediction errore2
Direction
less thane2
Comparator
calibration errore2

Verbatim

t0

“Predictors of Alfalfa Forage Quality”

✓ located verbatim in the source · title, characters 0–36
e2

“the prediction error frequently was less than the calibration error”

✓ located verbatim in the source · abstract, characters 1110–1177

Title

Predictors of Alfalfa Forage Quality: Validation with Field Data

Matt A. Sanderson Crop Science 1992. doi:10.2135/cropsci1992.0011183x003200010049x

Other claims from this paper (5)
Method / resourceobservedoriginal resultnegativemeasured

Claim · model-written summary

EONR prediction error was about three times higher than yield prediction error across forecast times.

Outcome
prediction error of EONRe0
Direction
highere0
Value
about three timese0
Comparator
yield predictionse0

Verbatim

e0

“Across all forecast times, prediction error of EONR was about three times higher than yield predictions.”

✓ located verbatim in the source · abstract, characters 1789–1893
t0

“Corn”

✓ located verbatim in the source · title, characters 40–44

Title

A Systems Modeling Approach to Forecast Corn Economic Optimum Nitrogen Rate

Laila A. Puntel; John E. Sawyer; Daniel W. Barker; et al. Frontiers in Plant Science 2018. doi:10.3389/fpls.2018.00436

Other claims from this paper (6)
Method / resourceobservedoriginal resultmeasured

Claim · model-written summary

The root-mean-square error of prediction and the standard error of prediction were both 0.0026.

Taxon
foxtail milletSetaria italicae2
Outcome
the root-means-square error of prediction (RMSEP) and the standard error of prediction (SEP)e1
Value
0.0026e1

Verbatim

e1

“Accordingly, the root-means-square error of prediction (RMSEP) and the standard error of prediction (SEP) are both 0.0026”

✓ located verbatim in the source · abstract, characters 1026–1147
e2

“Seven varieties of foxtail millet were successfully identified”

✓ located verbatim in the source · abstract, characters 510–572

Title

Identification of Foxtail Millet Varieties Using Leaf Surface Spectral Information

Xiaoping Han; Wei Yang; Haiyan Song; et al. Sensors and Materials 2020. doi:10.18494/sam.2020.2718

Other claims from this paper (5)

Physiology, metabolism & abiotic stress 1562

Show all 1,562 →
Method / resourceobservedoriginal resultmeasured

Claim · model-written summary

The average prediction error of the model was within 14% of the mean absolute error.

Outcome
average prediction errore5
Value
14%e5
Comparator
mean absolute errore5

Verbatim

e5

“The average prediction error was within 14% of the mean absolute error”

✓ located verbatim in the source · abstract, characters 2532–2602

Title

A novel reflectance-based model for evaluating chlorophyll concentrations of fresh and water-stressed leaves

C. Lin; S. C. Popescu; S. C. Huang; et al. Biogeosciences 2015. doi:10.5194/bg-12-49-2015

Other claims from this paper (4)
Method / resourceobservedoriginal resultmeasured

Claim · model-written summary

Standard error of prediction was 0.562.

Taxon
Fuji applee0
Outcome
standard error of predictione1
Value
0.562e1

Verbatim

e0

“Fuji apple”

✓ located verbatim in the source · abstract, characters 143–153
e1

“standard error of prediction (SEP) 0.562”

✓ located verbatim in the source · abstract, characters 710–750

Title

Nondestructive measurement of reducing sugar of apples using Vis/NIR spectroscopy techniques

Yihong Jiang; Xiaoli Li; Xiujun Bai; et al. Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE 2006. doi:10.1117/12.710953

Other claims from this paper (3)
Method / resourceobservedoriginal resultnegativesignificant (as stated)measured

Claim · model-written summary

Direct prediction of in-vitro digestibility had significantly more error than prediction of ADF and NDF.

Taxon
alfalfaMedicago sativat0
Outcome
errore0
Direction
increasese0
Comparator
prediction of ADF and NDFe0

Verbatim

e0

“significant increases in error compared to prediction of ADF and NDF”

✓ located verbatim in the source · abstract, characters 1480–1548
t0

“alfalfa”

✓ located verbatim in the source · title, characters 62–69

Title

Predicting forage nutritive value from height and maturity of alfalfa in Saskatchewan, Canada

Korvin Arthur Grant Olfert University Library - University of Saskatchewan (University of Saskatchewan) 2003

Other claims from this paper (6)

Woody plants 682

Show all 682 →
Method / resourceobservedoriginal resultmeasured

Claim · model-written summary

The volume prediction standard error was 9.41.

Outcome
standard error of predictione0
Value
9.41e0

Verbatim

e0

“the standard error of prediction was 9.41”

✓ located verbatim in the source · abstract, characters 615–656
t0

“mango”

✓ located verbatim in the source · title, characters 22–27

Title

Volume measurement of mango using acoustic resonance technique

Amorndej Puttipipatkajorn http://www.tci-thaijo.org/index.php/kkuenj/article/download/21686/18704/ 2013. doi:10.14456/kkuenj.2013.35

Other claims from this paper (2)
Method / resourceobservedoriginal resultpositivemeasured

Claim · model-written summary

The confusion matrix showed a very low prediction error rate.

Outcome
prediction error ratee0
Qualifier
very lowe0

Not reportedComparatorValue

Verbatim

e0

“the confusion matrix shows a very low prediction error rate”

✓ located verbatim in the source · abstract, characters 1269–1328

Title

Clove Quality Classification using the ResNet50V2 Architecture

Muhamad Nurfaizi Linggama; Dhani Ariatmanto SISTEMASI 2025. doi:10.32520/stmsi.v14i2.5075

Other claims from this paper (3)
Method / resourceobservedoriginal resultmeasured

Claim · model-written summary

The modified model reduced volume prediction error by 70%.

Taxon
Pinus taedaPinus taedat0
Outcome
prediction errore0
Direction
reducede0
Value
70%e0

Verbatim

e0

“the prediction of the volume reduced the prediction error by 70%”

✓ located verbatim in the source · abstract, characters 579–643
t0

“PINUS TAEDA”

✓ located verbatim in the source · title, characters 61–72

Title

MODELAGEM NÃO LINEAR PARA DESCREVER O AFILAMENTO DO FUSTE EM PINUS TAEDA

Marcos Felipe NICOLETTI; Samuel Padua Chaves CARVALHO; Sebastião do Amaral MACHADO; et al. Brazilian Journal of Biometrics 2018. doi:10.28951/rbb.v36i4.310

Other claims from this paper (2)

Development, reproduction & morphology 248

Show all 248 →
Method / resourceobservedoriginal resultmeasured

Claim · model-written summary

Prediction error ranged from zero to eight days.

Outcome
error of predictione0
Value
0 to 8 dayse0

Not reportedTaxon

Verbatim

e0

“Overall, the error of prediction ranged from 0 to 8 days”

✓ located verbatim in the source · abstract, characters 779–835

Title

Developing Phenological Prediction Tables for Soybean

Lingxiao Zhang; Jiuquang Zhang; Clarence E. Watson; et al. Crop Management 2004. doi:10.1094/cm-2004-1025-01-rs

Other claims from this paper (2)
Method / resourceobservedoriginal resultpositivemeasured

Claim · model-written summary

The approach reduced wheat maturity prediction error.

Outcome
prediction error of wheat maturitye0
Direction
reducede0

Not reportedComparatorValue

Verbatim

e0

“experiment reduced the prediction error of wheat maturity”

✓ located verbatim in the source · abstract, characters 1002–1059

Title

Unmanned Aerial Vehicle Application of Multispectral Sensor Data in Agriculture

Xintong Han; Yike Sang; Songling Zhao 2024 International Conference on Machine Intelligence and Digital Applications 2024. doi:10.1145/3662739.3672308

Other claims from this paper (3)
Method / resourceobservedoriginal resultpositivemeasured

Claim · model-written summary

Prediction error was below 5%.

Taxon
sunflower and potatoSolanum tuberosumHelianthus annuuse0
Outcome
prediction errore1
Value
5%e1

Not reportedComparator

Verbatim

e0

“development stages of sunflower and potato”

✓ located verbatim in the source · abstract, characters 308–350
e1

“prediction error lower than 5%”

✓ located verbatim in the source · abstract, characters 832–862

Title

[Simulation model for the crop development stages in sunflower-potato intercropping].

Fang Gou; Li-Zhen Zhang; Wan-Lin Dong; et al. PubMed 2012

Other claims from this paper (5)

Plant interactions & biotic stress 211

Show all 211 →
Method / resourceobservedoriginal resultpositivemeasured

Claim · model-written summary

The proposed model had a minimal prediction error of 0.15.

Outcome
prediction errore0
Value
0.15e0

Verbatim

e0

“a minimal prediction error of 0.15”

✓ located verbatim in the source · abstract, characters 1818–1852

Title

Enhanced CNN for Fruit Disease Detection and Grading Classification Using SSDAE-SVM for Postharvest Fruits

Himanshu B. Patel; Nitin J. Patil IEEE Sensors Journal 2023. doi:10.1109/jsen.2023.3342833

Other claims from this paper (4)
Method / resourceobservedoriginal resultmeasured

Claim · model-written summary

The SA-CCT model had mean square error of 0.2357 for blackpod prediction.

Outcome
Mean Square Errore0
Value
0.2357e0

Verbatim

e0

“Mean Square Error: 0.2357”

✓ located verbatim in the source · abstract, characters 952–977

Title

Early Cocoa Blackpod Pathogen Prediction with Machine Learning Ensemble Algorithm based on Climatic Parameters

Olofintuyi Sunday Samuel DOAJ (DOAJ: Directory of Open Access Journals) 2022

Other claims from this paper (3)
Method / resourceobservedoriginal resultmeasured

Claim · model-written summary

The Australian model had 21·9% overall prediction error.

Taxon
Stylosanthes scabraStylosanthes scabrae1
Outcome
overall prediction errore0
Value
21·9%e0

Not reportedMaterial

Verbatim

e0

“the overall prediction error was 21·9% for the Australian and 22·1% for the South American model”

✓ located verbatim in the source · abstract, characters 814–910
e1

“Stylosanthes scabra caused by Colletotrichum gloeosporioides”

✓ located verbatim in the source · abstract, characters 183–243

Title

Weather‐based prediction of anthracnose severity using artificial neural network models

S. Chakraborty; R. Ghosh; M. Ghosh; et al. Plant Pathology 2004. doi:10.1111/j.1365-3059.2004.01044.x

Other claims from this paper (8)

Molecular genetics & genomics 76

Show all 76 →
Method / resourceobservedoriginal resultpositivemeasured

Claim · model-written summary

Accuracy from prediction error variance was a useful indicator of genomic prediction accuracy.

Outcome
accuracy of predictione0
Comparator
accuracy of genomic breeding values from the prediction error variancee0

Not reportedTaxon

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

Title

Development of Genomic Prediction in Sorghum

Colleen H. Hunt; Fred A. van Eeuwijk; Emma S. Mace; et al. Crop Science 2018. doi:10.2135/cropsci2017.08.0469

Other claims from this paper (5)
Method / resourceobservedoriginal resultmeasured

Claim · model-written summary

SPASIBA had median spatial prediction error of 188.7 km.

Taxon
Cedrela odorataCedrela odoratae1
Outcome
median prediction errore0
Value
188.7 kme0

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

Title

Predicting the geographic origin of Spanish Cedar (Cedrela odorata L.) based on DNA variation

Kristen N. Finch; Richard C. Cronn; Marianella C. Ayala Richter; et al. Conservation Genetics 2020. doi:10.1007/s10592-020-01282-6

Other claims from this paper (4)
Method / resourceobservedoriginal resultpositivemeasured

Claim · model-written summary

Multiple covariates can improve prediction accuracy or reduce normalized root mean square error.

Outcome
prediction accuracy or reduce the normalized root mean square errore0
Direction
improvee0

Not reportedComparator

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

Title

Integration of physiological and remote sensing traits for improved genomic prediction of wheat yield

Guillermo García‐Barrios; Carlos A. Robles‐Zazueta; Abelardo Montesinos‐López; et al. The Plant Genome 2025. doi:10.1002/tpg2.70110

Other claims from this paper (7)

Ecology, evolution & diversity 59

Show all 59 →
Method / resourceobservedoriginal resultmeasured

Claim · model-written summary

Mean absolute prediction error was 13%.

Outcome
mean absolute prediction errore0
Value
13%e0

Verbatim

e0

“with a mean absolute prediction error of only 13%”

✓ located verbatim in the source · abstract, characters 1751–1800

Title

Testing the generality of above‐ground biomass allometry across plant functional types at the continent scale

Keryn I. Paul; Stephen H. Roxburgh; Jerome Chave; et al. Global Change Biology 2015. doi:10.1111/gcb.13201

Other claims from this paper (8)
Method / resourceobservedoriginal resultpositivemeasured

Claim · model-written summary

Validation prediction error variance was 0.042.

Taxon
foragee2
Outcome
prediction error variancee0
Value
0.042e0

Verbatim

e0

“prediction error variance was 0.042”

✓ located verbatim in the source · abstract, characters 1692–1727
t0

“Northern Mixed-Grass Prairie”

✓ located verbatim in the source · title, characters 35–63
e2

“N content of forage using dead:green ratios”

✓ located verbatim in the source · abstract, characters 299–342

Title

Predicting Nitrogen Content in the Northern Mixed-Grass Prairie

M. R. Haferkamp; M. D. MacNeil; E. E. Grings; et al. Journal of Range Management 2005. doi:10.2458/azu_rangelands_v58i2_macneil

Other claims from this paper (6)
Method / resourceobservedoriginal resultpositivemeasured

Claim · model-written summary

The fitted equation had prediction error variance of 0.026.

Taxon
foragee2
Outcome
prediction error variancee0
Value
0.026e0

Verbatim

e0

“prediction error variance was 0.026”

✓ located verbatim in the source · abstract, characters 1055–1090
t0

“Northern Mixed-Grass Prairie”

✓ located verbatim in the source · title, characters 35–63
e2

“N content of forage using dead:green ratios”

✓ located verbatim in the source · abstract, characters 299–342

Title

Predicting Nitrogen Content in the Northern Mixed-Grass Prairie

M. R. Haferkamp; M. D. MacNeil; E. E. Grings; et al. Journal of Range Management 2005. doi:10.2458/azu_rangelands_v58i2_macneil

Other claims from this paper (6)

not stated 1

Recommendationauthor interpretationoriginal resultpositivebasis n/a

Claim · model-written summary

The authors recommend improving the survey design and decreasing measurement error.

Outcome
improve the survey design and decrease measurement errore7

Verbatim

t0

“cassava mosaic virus disease”

✓ located verbatim in the source · title, characters 72–100
e7

“Large relative prediction errors obtained in the regression kriging procedure show the need to improve the survey design and decrease measurement error”

✓ located verbatim in the source · abstract, characters 1118–1269

Title

Comparison of disease patterns assessed by three independent surveys of cassava mosaic virus disease in Rwanda and Burundi

H. Bouwmeester; G. B. M. Heuvelink; J. P. Legg; et al. Plant Pathology 2011. doi:10.1111/j.1365-3059.2011.02500.x

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Other claims from this paper (12)

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