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Taxon: rice ✕

Taxon groups are the verbatim names found in the abstracts (case-insensitive), not normalised: maize, corn and Zea mays are separate groups here. The Organism view merges them through NCBI Taxonomy.

Method / resourceauthor interpretationoriginal resultmeasured

Claim · model-written summary

A curve decrease at 420 nm preceded pollen abortion, suggesting a marker.

Outcome
marker associated with pollen abortione0
Direction
decreasee0
Value
420 nme0

Verbatim

e0

“a steep decrease of multispectral curves at 420 nm appeared before the occurrence of abortion, implying a marker associated with pollen abortion in rice”

✓ located verbatim in the source · abstract, characters 976–1128

Title

Study of the Changes in Insoluble Polysaccharides during Pollen Development in Rice (Oryza sativa L.) by Microscopic Multispectral Imaging

Shunan Liu; Lei Pan; Qiongshui Wu; et al. Microscopy and Microanalysis 2010. doi:10.1017/s1431927610000413

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

Claim · model-written summary

The rice silicon model reproduced a gradual decrease in transporter gene expression observed previously.

Outcome
expression level of the transporter genese0
Direction
gradual decreasee0
Comparator
previous experimental studiese0

Verbatim

e0

“The model reproduced the gradual decrease and diurnal variation of the expression level of the transporter genes observed by previous experimental studies”

✓ located verbatim in the source · abstract, characters 809–963
t0

“Rice”

✓ located verbatim in the source · title, characters 31–35

Title

A Model of Silicon Dynamics in Rice: An Analysis of the Investment Efficiency of Si Transporters

Gen Sakurai; Naoki Yamaji; Namiki Mitani-Ueno; et al. Frontiers in Plant Science 2017. doi:10.3389/fpls.2017.01187

Other claims from this paper (3)
Method / resourceobservedoriginal resultno effectnot significant (as stated)measured

Claim · model-written summary

ePE6d precise small edits did not decrease significantly relative to ePE2.

Outcome
precise edits by ePE6de0
Direction
decreasee0

Not reportedComparatorValue

Verbatim

t0

“rice”

✓ located verbatim in the source · title, characters 60–64
e0

“a significant decrease in precise edits by ePE6d was not observed throughout the targets”

✓ located verbatim in the source · abstract, characters 2001–2089

Title

Engineering PE6 prime editors to efficiently insert tags in rice

Rongfang Xu; Chong Ma; Jiaqi Sheng; et al. Plant Biotechnology Journal 2024. doi:10.1111/pbi.14456

Other claims from this paper (19)
Method / resourceobservedoriginal resultnegativemeasured

Claim · model-written summary

Low-resolution testing gave a 2% decrease in metrics compared with high-resolution scenarios.

Outcome
metricse2
Direction
decreasee2
Value
2%e2
Comparator
high-resolution scenariose2

Verbatim

e0

“Detecting diseases on rice leaves plays a crucial role”

✓ located verbatim in the source · abstract, characters 0–54
e2

“Notably, Tiny-CNN trained on high-resolution images excels, achieving over 98% across all metrics when tested on low-resolution images, with a 2% decrease compared to high-resolution scenarios”

✓ located verbatim in the source · abstract, characters 758–950

Title

Tiny-CNN: Structuring Convolutional Neural Networks for Accurate Classification of Rice Leaf Diseases in Resource-Constrained Environments

Khang Quoc Nguyen; Huy Cao Gia Nguyen; Trinh Ngo Diem Le; et al. Proceedings of the 2024 9th International Conference on Intelligent Information Technology 2024. doi:10.1145/3654522.3654570

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

Claim · model-written summary

The model reduced FLOPs by 50% relative to YOLOv5s.

Outcome
FLOPse0
Direction
decreasee0
Value
50%e0
Comparator
YOLOv5se0

Verbatim

e0

“The experimental results showed that the designed model had a 49% decrease in the number of model parameters and a 50% decrease in FLOPs without a decrease in precision on self-built rice blast dataset, compared with the YOLOv5s”

✓ located verbatim in the source · abstract, characters 688–916

Title

A LIGHTWEIGHT IMPROVED YOLOv5s MODEL-BASED RICE BLAST DETECTION METHOD AND MOBILE DEPLOYMENT

Fankai MENG; Congkuan YAN; Yuqing YANG; et al. INMATEH Agricultural Engineering 2024. doi:10.35633/inmateh-74-68

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

Claim · model-written summary

The model reduced parameter count by 49% relative to YOLOv5s.

Outcome
number of model parameterse0
Direction
decreasee0
Value
49%e0
Comparator
YOLOv5se0

Verbatim

e0

“The experimental results showed that the designed model had a 49% decrease in the number of model parameters and a 50% decrease in FLOPs without a decrease in precision on self-built rice blast dataset, compared with the YOLOv5s”

✓ located verbatim in the source · abstract, characters 688–916

Title

A LIGHTWEIGHT IMPROVED YOLOv5s MODEL-BASED RICE BLAST DETECTION METHOD AND MOBILE DEPLOYMENT

Fankai MENG; Congkuan YAN; Yuqing YANG; et al. INMATEH Agricultural Engineering 2024. doi:10.35633/inmateh-74-68

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

Claim · model-written summary

The model simulated tiller increase and decrease well across ecological and cultural conditions.

Outcome
dynamic changes in the tiller increase and decreasee0

Not reportedComparatorValue

Verbatim

t0

“rice”

✓ located verbatim in the source · title, characters 36–40
e0

“Validation of the model using the experiment data from a wide range of ecological and cultural conditions showed that the model could well simulate the dynamic changes in the tiller increase and decrease.”

✓ located verbatim in the source · abstract, characters 627–831

Title

Simulation on the tiller dynamic in rice

Meng Ya Nanjing Nongye Daxue xuebao 2003

Other claims from this paper (1)
Method / resourceobservedoriginal resultno effectmeasured

Claim · model-written summary

The model maintained precision relative to YOLOv5s.

Outcome
precisione0
Direction
without a decreasee0
Comparator
YOLOv5se0

Verbatim

e0

“without a decrease in precision on self-built rice blast dataset, compared with the YOLOv5s”

✓ located verbatim in the source · abstract, characters 825–916

Title

A LIGHTWEIGHT IMPROVED YOLOv5s MODEL-BASED RICE BLAST DETECTION METHOD AND MOBILE DEPLOYMENT

Fankai MENG; Congkuan YAN; Yuqing YANG; et al. INMATEH Agricultural Engineering 2024. doi:10.35633/inmateh-74-68

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

Claim · model-written summary

Field testing found a significant decrease in bird numbers in targeted areas.

Interactor
birdsAvese0pest
Outcome
number of birds in the targeted arease0
Direction
decreasinge0
Value
significante0

Verbatim

e0

“the result of the prototype field-testing showed a significant effect in decreasing the number of birds in the targeted areas”

✓ located verbatim in the source · abstract, characters 1282–1407
e1

“Filipino rice farmers, resulting in losses in annual harvest yield. Farmers suffer the challenge of crop damage by birds”

✓ located verbatim in the source · abstract, characters 45–165

Title

Bird-Repelling Device for Agricultural Application using Motion Detection

Maribelle Dequilla-Pabiania; Chris Julian C. Alcedo; Henry G. Morales; et al. 2024 16th International Conference on Computer and Automation Engineering (ICCAE) 2024. doi:10.1109/iccae59995.2024.10569619

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

Claim · model-written summary

The system reduced nitrogen consumption by 25%.

Outcome
nitrogen consumptione0
Direction
decreasee0
Value
25%e0

Verbatim

e0

“25% decrease in nitrogen consumption”

✓ located verbatim in the source · abstract, characters 1060–1096
t0

“Rice Cultivation”

✓ located verbatim in the source · title, characters 111–127

Title

Integrating IOTA’s Tangle with the Internet of Things for Sustainable Agriculture: A Proof-of-Concept Study on Rice Cultivation

Sandro Pullo; Remo Pareschi; Valentina Piantadosi; et al. Informatics 2023. doi:10.3390/informatics11010003

Other claims from this paper (5)

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