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Method / resourceobservedoriginal resultpositivesimulated

Claim · model-written summary

Under current climatic conditions, total global crop production could increase by 28%, from 4 200 to 5 400 mio. t DM, if multiple cropping is expanded on existing cropland.

Taxon: crope1
Outcome: Total global crop productione1
Direction: increasee1
Value: 28%e1

Verbatim

e1
“Total global crop production could increase by 28% (from 4 200 mio. t DM to 5 400 mio. t DM)”
✓ located verbatim in the source · abstract, characters 1066–1158

Title

Irrigation constraints shape the global potential for multiple cropping expansion on existing cropland

Felicitas D Beier; Jens Heinke; Benjamin Leon Bodirsky; et al. Environmental Research Letters 2026. doi:10.1088/1748-9326/ae44ae

Other claims from this paper (4)

Method / resourceobservedoriginal resultbasis n/a

Claim · model-written summary

Thirteen repeat-sensitive genome assemblies of maize and its recent ancestor teosinte were generated.

Taxon: maizeZea mayse0
Outcome: repeat-sensitive genome assembliese0
Value: 13e0

Verbatim

t0
“maize”
✓ located verbatim in the source · title, characters 71–76

e0
“we generate 13 repeat-sensitive genome assemblies of maize and its recent ancestor, teosinte”
✓ located verbatim in the source · abstract, characters 623–715

Title

Higher order repeat structures reflect diverging evolutionary paths in maize centromeres and knobs

Rebecca D. Piri; Mingyu Wang; M. Cinta Romay; et al. Genome biology 2026. doi:10.1186/s13059-026-03993-4

Other claims from this paper (9)

Method / resourceobservedoriginal resultpositivebasis n/a

Claim · model-written summary

A new graph-based pipeline, HiReNET, was developed to classify higher-order repeats and was shown to work in Arabidopsis and maize.

Taxon: Arabidopsis and maizeArabidopsis Zea mayse0
Outcome: classify HORse0

Not reportedComparator

Verbatim

e0
“We develop a new graph-based pipeline, HiReNET, to classify HORs and demonstrate its utility in both Arabidopsis and maize”
✓ located verbatim in the source · abstract, characters 717–839

Title

Higher order repeat structures reflect diverging evolutionary paths in maize centromeres and knobs

Rebecca D. Piri; Mingyu Wang; M. Cinta Romay; et al. Genome biology 2026. doi:10.1186/s13059-026-03993-4

Other claims from this paper (9)

Method / resourceobservedoriginal resultpositivemeasured

Claim · model-written summary

The 3D-LC/HRMS workflow enabled identification or tentative characterization of 170 compounds from Centella asiatica.

Taxon: C. asiaticaCentella asiaticae0
Outcome: identification or tentative characterizatione0
Value: 170 compoundse0

Verbatim

e0
“enabled the identification or tentative characterization of 170 compounds from C. asiatica”
✓ located verbatim in the source · abstract, characters 872–962

Title

Offline three-dimensional chromatography coupled with high-definition data-independent acquisition of ion mobility quadrupole time-of-flight mass spectrometry enabling the multicomponent characterization of Centella asiatica

Yi-yun Dang; Min Zhang; Lian-xing Wang; et al. Journal of Food Composition and Analysis 2026. doi:10.1016/j.jfca.2026.109000

Other claims from this paper (4)

Method / resourceobservedoriginal resultpositivemeasured

Claim · model-written summary

Collision-cross section value determination and prediction further resolved seven groups of isomeric compounds.

Outcome: resolved seven groups of isomeric compoundse0
Value: seven groupse0

Verbatim

e0
“Collision-cross section (CCS) value determination and prediction further resolved seven groups of isomeric compounds”
✓ located verbatim in the source · abstract, characters 1009–1125

Title

Offline three-dimensional chromatography coupled with high-definition data-independent acquisition of ion mobility quadrupole time-of-flight mass spectrometry enabling the multicomponent characterization of Centella asiatica

Yi-yun Dang; Min Zhang; Lian-xing Wang; et al. Journal of Food Composition and Analysis 2026. doi:10.1016/j.jfca.2026.109000

Other claims from this paper (4)

Method / resourceobservedoriginal resultpositivemeasured

Claim · model-written summary

The proposed method achieves superior accuracy and stability compared with existing chrysanthemum classification methods.

Taxon: ChrysanthemumChrysanthemumt0
Outcome: accuracy and stabilitye0
Direction: superiore0
Comparator: existing classification methodse0

Verbatim

t0
“Chrysanthemum classification”
✓ located verbatim in the source · title, characters 0–28

e0
“the proposed method achieves superior accuracy and stability compared to existing classification methods”
✓ located verbatim in the source · abstract, characters 1040–1144

Title

Chrysanthemum classification via color space fusion transformer

Jian Jiang; Xichen Yang; Tianshu Wang; et al. Scientific Reports 2026. doi:10.1038/s41598-026-40027-2

Other claims from this paper (1)

Method / resource The proposed method achieves a classification accuracy of 96.16%.

Method / resourceobservedoriginal resultpositivemeasured

Claim · model-written summary

The proposed method achieves a classification accuracy of 96.16%.

Taxon: ChrysanthemumChrysanthemumt0
Outcome: classification accuracye0
Value: 96.16%e0

Verbatim

t0
“Chrysanthemum classification”
✓ located verbatim in the source · title, characters 0–28

e0
“a classification accuracy of 96.16%”
✓ located verbatim in the source · abstract, characters 1151–1186

Title

Chrysanthemum classification via color space fusion transformer

Jian Jiang; Xichen Yang; Tianshu Wang; et al. Scientific Reports 2026. doi:10.1038/s41598-026-40027-2

Other claims from this paper (1)

Method / resource The proposed method achieves superior accuracy and stability compared with existing chrysanthemum classification methods.

Method / resourceobservedoriginal resultpositivemeasured

Claim · model-written summary

IFSA achieved significantly higher early rice blast detection accuracy than conventional single-method dimensionality reduction approaches.

Taxon: riceOryza sativae0
Material: Yanfeng 47e0
Outcome: detection accuracye1
Direction: highere1
Comparator: conventional single-method dimensionality reduction approachese1

Verbatim

e0
“Field experiments were conducted in Haicheng, Anshan, Liaoning Province, China, using the rice cultivar Yanfeng 47”
✓ located verbatim in the source · abstract, characters 608–722

e1
“achieving significantly higher detection accuracy than conventional single-method dimensionality reduction approaches”
✓ located verbatim in the source · abstract, characters 938–1055

Title

IFSA-Inception-CBAM: An Early Detection Model for Rice Blast Disease Based on Integrated Feature Selection and a Deep Convolutional Neural Network

Dongxue Zhao; Zetong Fu; Qi Liu; et al. Agriculture 2026. doi:10.3390/agriculture16040468

Other claims from this paper (2)

Method / resourceobservedoriginal resultpositivemeasured

Claim · model-written summary

Inception-CBAM reached an overall accuracy of 95.44 ± 0.50% for early rice blast detection, outperforming six baseline models.

Taxon: riceOryza sativae0
Material: Yanfeng 47e0
Outcome: overall accuracye1
Direction: outperforminge3
Value: 95.44 ± 0.50%e1
Comparator: six baseline modelse2

Verbatim

e0
“Field experiments were conducted in Haicheng, Anshan, Liaoning Province, China, using the rice cultivar Yanfeng 47”
✓ located verbatim in the source · abstract, characters 608–722

e1
“Inception-CBAM achieved an overall accuracy of 95.44 ± 0.50%”
✓ located verbatim in the source · abstract, characters 1472–1532

e2
“Compared with six baseline models”
✓ located verbatim in the source · abstract, characters 1350–1383

e3
“outperforming all competing methods”
✓ located verbatim in the source · abstract, characters 1614–1649

Title

IFSA-Inception-CBAM: An Early Detection Model for Rice Blast Disease Based on Integrated Feature Selection and a Deep Convolutional Neural Network

Dongxue Zhao; Zetong Fu; Qi Liu; et al. Agriculture 2026. doi:10.3390/agriculture16040468

Other claims from this paper (2)

Method / resourceobservedoriginal resultpositivemeasured

Claim · model-written summary

Inception-CBAM reached a Kappa coefficient of 93.92 ± 0.67% for early rice blast detection, outperforming six baseline models.

Taxon: riceOryza sativae0
Material: Yanfeng 47e0
Outcome: Kappa coefficiente1
Direction: outperforminge3
Value: 93.92 ± 0.67%e1
Comparator: six baseline modelse2

Verbatim

e0
“Field experiments were conducted in Haicheng, Anshan, Liaoning Province, China, using the rice cultivar Yanfeng 47”
✓ located verbatim in the source · abstract, characters 608–722

e1
“a Kappa coefficient of 93.92 ± 0.67%”
✓ located verbatim in the source · abstract, characters 1537–1573

e2
“Compared with six baseline models”
✓ located verbatim in the source · abstract, characters 1350–1383

e3
“outperforming all competing methods”
✓ located verbatim in the source · abstract, characters 1614–1649

Title

IFSA-Inception-CBAM: An Early Detection Model for Rice Blast Disease Based on Integrated Feature Selection and a Deep Convolutional Neural Network

Dongxue Zhao; Zetong Fu; Qi Liu; et al. Agriculture 2026. doi:10.3390/agriculture16040468

Other claims from this paper (2)

Method / resource 684,532 claims

684,532 claims