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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)
Method / resourceobservedoriginal resultmeasured

Claim · model-written summary

The spatial error model had prediction error around 0.353 Mt/ha.

Outcome
prediction errore0
Value
0.353 Mt/hae0

Verbatim

e0

“prediction error of around 0.353 Mt/ha when employing the Spatial Error model”

✓ located verbatim in the source · abstract, characters 1264–1341
t0

“Rice”

✓ located verbatim in the source · title, characters 0–4

Title

Rice Yield Estimation Based on SAR and Meteorological Parameters

Digvijaya Paudel; Bikash Sherchan; Krishna Prasad Bhandari Journal of Engineering and Sciences 2023. doi:10.3126/jes2.v2i1.60373

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

Claim · model-written summary

The prediction had an error threshold.

Taxon
Paddyt0
Outcome
error threshold in the predictione0

Not reportedValue

Verbatim

e0

“there is an error threshold in the prediction”

✓ located verbatim in the source · abstract, characters 1825–1870
t0

“Paddy Yield Prediction”

✓ located verbatim in the source · title, characters 42–64

Title

Artificial Neural Network to Estimate the Paddy Yield Prediction Using Climatic Data

Vinushi Amaratunga; Lasini Wickramasinghe; Anushka Perera; et al. Mathematical Problems in Engineering 2020. doi:10.1155/2020/8627824

Other claims from this paper (4)
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 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

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 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

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)

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