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Method / resourceobservedoriginal resultmeasured

Claim · model-written summary

Missing data caused only a minor decrease in selection coincidence.

Taxon
maizeZea mayst0
Outcome
coincidencee0
Direction
decreasee0

Not reportedComparatorValue

Verbatim

e0

“missing data only led to a minor decrease in coincidence.”

✓ located verbatim in the source · abstract, characters 1314–1371
t0

“Maize Hybrids”

✓ located verbatim in the source · title, characters 92–105

Title

Impact of Phenotypic Correction Method and Missing Phenotypic Data on Genomic Prediction of Maize Hybrids

Giovanni Galli; Danilo Hottis Lyra; Filipe Couto Alves; et al. Crop Science 2018. doi:10.2135/cropsci2017.07.0459

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

Claim · model-written summary

CPD-YOLO generalization showed a 0.55 % mAP decrease.

Outcome
mAPe0
Direction
decreasee0
Value
0.55 %e0

Not reportedComparator

Verbatim

e0

“the CPD-YOLO model had superior generalization capability, with a 1.30 % decrease in F1-score and a 0.55 % decrease in mAP.”

✓ located verbatim in the source · abstract, characters 1287–1410
t0

“cotton pests and diseases”

✓ located verbatim in the source · title, characters 48–73

Title

CPD-YOLO: A cross-platform detection method for cotton pests and diseases using UAV and smartphone imaging

Guowei Yu; Benxue Ma; Ruoyu Zhang; et al. Industrial Crops and Products 2025. doi:10.1016/j.indcrop.2025.121515

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

Claim · model-written summary

YOLOv8n-RML shows a 34.5% decrease in GFLOPS for strawberry ripeness detection.

Taxon
strawberryFragaria ×ananassae0
Outcome
GFLOPSe2
Direction
decreasee2
Value
34.5%e2

Verbatim

e0

“specifically tailored for strawberry harvesting”

✓ located verbatim in the source · abstract, characters 79–126
e2

“The YOLOv8n-RML model demonstrates a 48.6% reduction in parameters and a 34.5% decrease in GFLOPS”

✓ located verbatim in the source · abstract, characters 1002–1099

Title

A Lightweight Detection Algorithm for Strawberry Ripeness

Gaofeng Xu; Zhengcai Wang IEEE Access 2025. doi:10.1109/access.2025.3627896

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

Claim · model-written summary

CPD-YOLO generalization showed a 1.30 % F1-score decrease.

Outcome
F1-scoree0
Direction
decreasee0
Value
1.30 %e0

Not reportedComparator

Verbatim

e0

“the CPD-YOLO model had superior generalization capability, with a 1.30 % decrease in F1-score and a 0.55 % decrease in mAP.”

✓ located verbatim in the source · abstract, characters 1287–1410
t0

“cotton pests and diseases”

✓ located verbatim in the source · title, characters 48–73

Title

CPD-YOLO: A cross-platform detection method for cotton pests and diseases using UAV and smartphone imaging

Guowei Yu; Benxue Ma; Ruoyu Zhang; et al. Industrial Crops and Products 2025. doi:10.1016/j.indcrop.2025.121515

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

Claim · model-written summary

The 4M model predicted a considerable future decrease in maize yields.

Taxon
maizeZea mayse0
Outcome
maize yieldse0
Direction
decreasee0

Not reportedComparatorValue

Verbatim

e0

“4M predicted a considerable decrease of maize yields as well as yield safety for the future.”

✓ located verbatim in the source · abstract, characters 933–1025

Title

USING LONG-TERM FIELD EXPERIMENT DATA TO PREPARE A CROP SIMULATION MODEL FOR CLIMATE IMPACT STUDIES

GY. MICSKEI Applied Ecology and Environmental Research 2016. doi:10.15666/aeer/1403_263280

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

Claim · model-written summary

The 4M model predicted a considerable future decrease in maize yield safety.

Taxon
maizeZea mayse0
Outcome
yield safetye0
Direction
decreasee0

Not reportedComparatorValue

Verbatim

e0

“4M predicted a considerable decrease of maize yields as well as yield safety for the future.”

✓ located verbatim in the source · abstract, characters 933–1025

Title

USING LONG-TERM FIELD EXPERIMENT DATA TO PREPARE A CROP SIMULATION MODEL FOR CLIMATE IMPACT STUDIES

GY. MICSKEI Applied Ecology and Environmental Research 2016. doi:10.15666/aeer/1403_263280

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

Claim · model-written summary

YOLOv5_E accuracy shows a 0.9% marginal decrease compared with the original YOLOv5.

Taxon
pineappleAnanas comosuse0
Outcome
accuracye5
Direction
marginal decreasee5
Value
0.9%e5
Comparator
the original YOLOv5e5

Verbatim

e0

“During pineapple cultivation”

✓ located verbatim in the source · abstract, characters 9–37
e5

“Compared to the original YOLOv5, YOLOv5_E shows a 0.9% marginal decrease in accuracy”

✓ located verbatim in the source · abstract, characters 1033–1117

Title

A-pruning: a lightweight pineapple flower counting network based on filter pruning

Guoyan Yu; Ruilin Cai; Yingtong Luo; et al. Complex & Intelligent Systems 2023. doi:10.1007/s40747-023-01261-7

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

Claim · model-written summary

The two approaches gave a higher decrease rate with fewer samples.

Taxon
WatermelonCitrullus lanatust0
Outcome
decrease ratee0
Direction
highere0
Comparator
smaller quantitiese0

Verbatim

e0

“Testing different amount of samples, the two approaches can provide higher decrease rate in smaller quantities”

✓ located verbatim in the source · abstract, characters 810–920
t0

“Watermelon”

✓ located verbatim in the source · title, characters 36–46

Title

An Efficient Recognition Method for Watermelon using Faster R-CNN with Post-Processing

Ming-Jui Ho; Yu-Chin Lin; Hsin-Chuan Hsu; et al. 2019 8th International Conference on Innovation, Communication and Engineering (ICICE) 2019. doi:10.1109/icice49024.2019.9117374

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

Claim · model-written summary

Models with one and four augmentation techniques showed a decrease in performance.

Taxon
Tea Leaft0
Outcome
performancee0
Direction
decreasee0
Comparator
one and four augmentation techniquese0

Verbatim

t0

“Tea Leaf Disease Classification”

✓ located verbatim in the source · title, characters 83–114
e0

“models with one and four augmentation techniques experience a decrease in performance”

✓ located verbatim in the source · abstract, characters 889–974

Title

Comparative Analysis of Data Augmentation Techniques on MobileNetV2 Performance in Tea Leaf Disease Classification

Fitri Mintarsih; Dewi Khairani; M. Farhan 2024 12th International Conference on Cyber and IT Service Management (CITSM) 2024. doi:10.1109/citsm64103.2024.10775557

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

Claim · model-written summary

All four models had a slight accuracy decrease on workstations and mobile devices.

Taxon
Plantt0
Outcome
accuracye1
Direction
decreasee0
Comparator
workstation and mobile devicese0

Verbatim

e0

“the four models have a slight accuracy decrease on both workstation and mobile devices”

✓ located verbatim in the source · abstract, characters 765–851
e1

“Mason model has a highest accuracy of 90.54%”

✓ located verbatim in the source · abstract, characters 567–611
t0

“Plant Diseases”

✓ located verbatim in the source · title, characters 40–54

Title

Evaluation of CNN Models in Identifying Plant Diseases on a Mobile Device

Teddy Aristan; Gede Putra Kusuma Revue d intelligence artificielle 2023. doi:10.18280/ria.370221

Other claims from this paper (7)

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