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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 resultpositivesignificant (as stated)measured

Claim · model-written summary

The approach significantly reduced inspection time and errors.

Outcome
inspection time and errorse0
Direction
reducese0

Not reportedComparator

Verbatim

e0

“This approach significantly reduces inspection time and errors”

✓ located verbatim in the source · abstract, characters 1347–1409
t0

“Apple”

✓ located verbatim in the source · title, characters 45–50

Title

Revolutionizing Smart Agriculture: Enhancing Apple Quality with Machine Learning

El Mehdi IYOUBI; RAJA EL BOQ; KENZA IZIKKI; et al. Data & Metadata 2024. doi:10.56294/dm2024.592

Other claims from this paper (1)
Method / resourceobservedoriginal resultpositivesignificant (as stated)measured

Claim · model-written summary

Prediction errors were significantly smaller for all risk categories.

Outcome
prediction errorse0
Direction
smallere0

Not reportedComparator

Verbatim

e0

“prediction errors for all risk categories were significantly smaller”

✓ located verbatim in the source · abstract, characters 702–770
t0

“Wheat”

✓ located verbatim in the source · title, characters 76–81

Title

A Machine Learning-Based Framework for Drought Prediction and Resilience in Wheat Cultivation

Shaik Mahammed Haneef; D. Lita Pansy; Shaik Mabasha; et al. 2024 2nd International Conference on Signal Processing, Communication, Power and Embedded System (SCOPES) 2024. doi:10.1109/scopes64467.2024.10990721

Other claims from this paper (4)
Method / resourceobservedoriginal resultpositivesignificant (as stated)measured

Claim · model-written summary

Fagus model prediction errors reached 13.0 days and were significantly below benchmark errors.

Taxon
FagusFaguse0
Outcome
Errors associated with the modelled predictionse0
Direction
lowere0
Value
13.0 dayse0
Comparator
overall mean (null model)e0

Verbatim

e0

“Errors associated with the modelled predictions are up to 13.0 days in Fagus and 10.3 days in Quercus, which are significantly lower than errors associated with (1) the overall mean (null model) or (2) previously published modelling frameworks”

✓ located verbatim in the source · abstract, characters 875–1118

Title

Modelling interannual and spatial variability of leaf senescence for three deciduous tree species in France

Nicolas Delpierre; Éric Dufrêne; Kamel Soudani; et al. EGU General Assembly Conference Abstracts 2010

Other claims from this paper (10)
Method / resourceobservedoriginal resultpositivesignificant (as stated)measured

Claim · model-written summary

Quercus model prediction errors reached 10.3 days and were significantly below benchmark errors.

Taxon
QuercusQuercuse0
Outcome
Errors associated with the modelled predictionse0
Direction
lowere0
Value
10.3 dayse0
Comparator
previously published modelling frameworkse0

Verbatim

e0

“Errors associated with the modelled predictions are up to 13.0 days in Fagus and 10.3 days in Quercus, which are significantly lower than errors associated with (1) the overall mean (null model) or (2) previously published modelling frameworks”

✓ located verbatim in the source · abstract, characters 875–1118

Title

Modelling interannual and spatial variability of leaf senescence for three deciduous tree species in France

Nicolas Delpierre; Éric Dufrêne; Kamel Soudani; et al. EGU General Assembly Conference Abstracts 2010

Other claims from this paper (10)
Method / resourceobservedoriginal resultsignificant (as stated)measured

Claim · model-written summary

Representativeness errors differed significantly across levels.

Outcome
representativeness errorse0
Direction
differencese0
Comparator
different levelse0

Not reportedTaxon

Verbatim

e0

“results show significant differences of representativeness errors at different levels.”

✓ located verbatim in the source · abstract, characters 959–1045
t0

“Leaf Area Index”

✓ located verbatim in the source · title, characters 82–97

Title

Evaluating Spatial Representativeness of Station Observations for Remotely Sensed Leaf Area Index Products

Baodong Xu; Jing Li; Qinhuo Liu; et al. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 2016. doi:10.1109/jstars.2016.2560878

Other claims from this paper (4)
Method / resourceobservedoriginal resultsignificant (as stated)measured

Claim · model-written summary

Evaluator errors varied significantly.

Outcome
errors made by evaluatorse0
Value
P ≤ 0·001e0

Verbatim

e0

“There was significant ( P ≤ 0·001) variability amongst the errors made by evaluators”

✓ located verbatim in the source · abstract, characters 944–1028
t0

“tomato”

✓ located verbatim in the source · title, characters 79–85

Title

Comparison of three diagrammatic keys for the quantification of late blight in tomato leaves

F. M. Corrêa; J. S. S. Bueno Filho; M. G. F. Carmo Plant Pathology 2009. doi:10.1111/j.1365-3059.2009.02140.x

Other claims from this paper (6)
Method / resourceobservedoriginal resultpositivesignificant (as stated)measured

Claim · model-written summary

DroneZaic techniques significantly reduced mosaic errors.

Taxon
maizeZea mayse1
Outcome
errors in the output mosaicse0
Direction
reducee0

Not reportedComparator

Verbatim

e0

“Together, these techniques significantly reduce errors in the output mosaics.”

✓ located verbatim in the source · abstract, characters 1038–1115
e1

“maize lines”

✓ located verbatim in the source · abstract, characters 1249–1260

Title

DroneZaic : A robust end‐to‐end pipeline for mosaicking freely flown aerial video of agricultural fields

Dewi Endah Kharismawati; Toni Kazic The Plant Phenome Journal 2025. doi:10.1002/ppj2.70033

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

Claim · model-written summary

Standard errors for all independent-variable subsets were reported significant.

Taxon
Horticultural Plantst0
Outcome
standard errorse0

Not reportedComparatorDirection

Verbatim

e0

“standard errors for all subsets of the independent variables were found to be significant at the p”

✓ located verbatim in the source · abstract, characters 1652–1750
t0

“Horticultural Plants”

✓ located verbatim in the source · title, characters 54–74

Title

Leaf Area Prediction Models (Uzçelik-I) For Different Horticultural Plants

Sezgin Uzun; Hüseyin Çelik DergiPark (Istanbul University) 1999

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

Claim · model-written summary

Optimized stereo matching significantly reduced disparity errors.

Outcome
disparity errorse0
Direction
reducinge0
Comparator
census stereo matching algorithme0

Verbatim

e0

“the census stereo matching algorithm was optimized, significantly reducing disparity errors”

✓ located verbatim in the source · abstract, characters 720–811
t0

“tomato”

✓ located verbatim in the source · title, characters 33–39

Title

Enhanced precision in greenhouse tomato recognition and localization: A study leveraging advances in Yolov5 and binocular vision technologies

Shuangyou Wang; Zhanying Shao; Yongjian Zhang Quality Assurance and Safety of Crops & Foods 2024. doi:10.15586/qas.v16i3.1472

Other claims from this paper (2)

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