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Method / resourceobservedoriginal resultnegativesignificant (as stated)simulated

Claim · model-written summary

Temperature data errors influenced both crop models significantly more than precipitation errors.

Taxon
cropt0
Outcome
influence on both modelse0
Direction
strongere0
Comparator
errors in precipitatione0

Verbatim

e0

“errors in temperature data have a significantly stronger influence on both models than errors in precipitation”

✓ located verbatim in the source · abstract, characters 521–631
t0

“a process-based crop model and a statistical crop model”

✓ located verbatim in the source · abstract, characters 451–506

Title

Comparing the effects of calibration and climate errors on a statistical crop model and a process-based crop model

James Watson; Andrew J. Challinor; Thomas E. Fricker; et al. Climatic Change 2014. doi:10.1007/s10584-014-1264-3

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

Claim · model-written summary

Partial occlusions commonly caused errors in blueberry detection models.

Taxon
blueberryt0
Outcome
errorse0
Qualifier
partial occlusionse0

Not reportedComparatorValue

Verbatim

e0

“partial occlusions commonly cause errors”

✓ located verbatim in the source · abstract, characters 762–802
t0

“Blueberry Detection”

✓ located verbatim in the source · title, characters 42–61

Title

Comprehensive Analysis of Model Errors in Blueberry Detection and Maturity Classification: Identifying Limitations and Proposing Future Improvements in Agricultural Monitoring

Cristhian A. Aguilera; Cristhian A. Aguilera; Carola Figueroa-Flores; et al. Agriculture 2023. doi:10.3390/agriculture14010018

Other claims from this paper (1)
Method / resourceobservedoriginal resultnegativemeasured

Claim · model-written summary

SSEB errors were 22%.

Taxon
banana crope1
Outcome
errorse0
Direction
greatere0
Value
22%e0

Not reportedComparator

Verbatim

e0

“SSEB exhibited greater errors at 22%”

✓ located verbatim in the source · abstract, characters 923–959
e1

“banana crop”

✓ located verbatim in the source · abstract, characters 421–432

Title

Obtaining the daily actual evapotranspiration through remote sensing techniques application in Brazilian Semiarid

Camilla Kassar Borges; Rayonil Gomes Carneiro; Cleber Assis dos Santos; et al. Biblioteca Digital da Memória Científica do INPE (National Institute for Space Research) 2021. doi:10.29150/jhrs.v11.1.p18-31

Other claims from this paper (4)
Method / resourceauthor interpretationoriginal resultnegativemeasured

Claim · model-written summary

Methods for leaf water potential may have similar temperature related errors.

Taxon
leafe0
Outcome
Similar errorse0

Not reportedComparatorValue

Verbatim

e0

“Similar errors may occur in methods for measuring leaf water potential”

✓ located verbatim in the source · abstract, characters 246–316

Title

Relative Turgidity of Leaves: Temperature Effects in Measurement

B. D. Millar Science 1966. doi:10.1126/science.154.3748.512

Other claims from this paper (1)
Method / resourceobservedoriginal resultnegativemeasured

Claim · model-written summary

APSIM yield predictions were sensitive to water balance carryover errors, sometimes causing large relative yield errors.

Outcome
predicted relative yieldse1
Qualifier
in some seasons large errorse1
Comparator
water balancee1

Not reportedDirection

Verbatim

e0

“growth of a wheat crop”

✓ located verbatim in the source · abstract, characters 924–946
e1

“Yield predictions with APSIM were sensitive to carry-over errors in the water balance from one season to the next, so that in some seasons large errors occurred in the predicted relative yields.”

✓ located verbatim in the source · abstract, characters 1860–2054

Title

Modelling water, nitrogen, and crop yield for a long-term fallow management experiment

ME Probert; BA Keating; JP Thompson; et al. Australian Journal of Experimental Agriculture 1995. doi:10.1071/ea9950941

Other claims from this paper (7)
Method / resourceobservedoriginal resultnegativemeasured

Claim · model-written summary

SSEB errors reached 22% in the vegetated areas.

Taxon
banana cropt0
Outcome
errorse0
Direction
greatere0
Value
22%e0

Verbatim

e0

“SSEB exhibited greater errors at 22%”

✓ located verbatim in the source · abstract, characters 924–960
t0

“banana crop”

✓ located verbatim in the source · abstract, characters 421–432

Title

Obtaining the daily actual evapotranspiration through remote sensing techniques application in Brazilian Semiarid

Camilla Kassar Borges; Rayonil Gomes Carneiro; Cleber Assis Dos Santos; et al. Journal of Hyperspectral Remote Sensing 2021. doi:10.29150/2237-2202.2021.249016

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

Claim · model-written summary

The other two laboratories made seven wheat hardness classification errors.

Outcome
errorse0
Value
seven errorse0
Qualifier
other twoe0

Verbatim

e0

“Six laboratories achieved a near perfect classification with only three errors in all, but the other two had seven errors between them”

✓ located verbatim in the source · abstract, characters 1720–1854
e1

“Wheat kernel hardness”

✓ located verbatim in the source · abstract, characters 1557–1578

Title

Collaborative evaluation of near infrared reflectance analysis for the determination of protein, moisture and hardness in wheat

Brian G. Osborne; Thomas Fearn Journal of the Science of Food and Agriculture 1983. doi:10.1002/jsfa.2740340919

Other claims from this paper (10)
Method / resourceobservedoriginal resultnegativemeasured

Claim · model-written summary

Adsorption errors were larger in turgid tissue.

Taxon
soybeanGlycine maxe1
Outcome
errors due to adsorptione0
Direction
largere0

Not reportedValue

Verbatim

e0

“the errors due to adsorption were larger with turgid tissue”

✓ located verbatim in the source · abstract, characters 722–781
e1

“soybean (Glycine max L. Merr.) leaf water potentials”

✓ located verbatim in the source · abstract, characters 356–408

Title

Errors in Measuring Water Potentials of Small Samples Resulting from Water Adsorption by Thermocouple Psychrometer Chambers

Jerry M. Bennett; Peter M. Cortes PLANT PHYSIOLOGY 1985. doi:10.1104/pp.79.1.184

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

Claim · model-written summary

Classification errors occurred mainly among visually similar species.

Taxon
Plantt0
Outcome
Classification errorse0
Value
high visual similaritiese0
Qualifier
species with high visual similaritiese0

Verbatim

e0

“Classification errors occurred mainly in species with high visual similarities.”

✓ located verbatim in the source · abstract, characters 1176–1255
t0

“Plant”

✓ located verbatim in the source · title, characters 10–15

Title

GreenEye: Plant Classification Using MobileNet V2

Muhammad Syamil Hamami; Muhammad Rihap Firdaus; Pancadrya Yashod Pasha; et al. CoreID Journal 2025. doi:10.60005/coreid.v3i3.138

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

Claim · model-written summary

Prediction errors were large above 9000 kg/ha.

Outcome
prediction errorse0
Value
9000 kg/hae0
Qualifier
largee0

Verbatim

e0

“with large prediction errors above 9000 kg/ha.”

✓ located verbatim in the source · abstract, characters 1413–1459

Title

Research on the application method of agricultural machinery engineering automation based on multimodal characteristics

Xianggeng Wang; Yujia Fan Applied Mathematics and Nonlinear Sciences 2024. doi:10.2478/amns-2024-2137

Other claims from this paper (6)

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