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Method / resourceobservedoriginal resultpositivemeasured

Claim · model-written summary

The tool identified non-canonical SSPs that lack traditional signal peptides.

Outcome: SSPs that lack traditional signal peptidese0

Not reportedValue

Verbatim

e0
“SSPs that lack traditional signal peptides”
✓ located verbatim in the source · abstract, characters 1081–1123

Title

S ² ‐ PepAnalyst : A Web Tool for Predicting Plant Small Signalling Peptides

Kelly L. Vomo‐Donfack; Mariem Abaach; Ana M. Luna; et al. Plant Biotechnology Journal 2026. doi:10.1111/pbi.70536

Other claims from this paper (4)

Method / resource The SSP prediction tool achieved high predictive accuracy of 99.5% when validated against experimentally confirmed peptides, while maintaining low false-negative rates.
Method / resource The tool successfully classified peptides into functionally distinct families such as CLE and RALF.
Method / resource The tool performed robustly across both model plants and agriculturally important species.
Method / resource The tool is provided as a freely available web resource.

Method / resourceobservedoriginal resultpositivemeasured

Claim · model-written summary

The tool performed robustly across both model plants and agriculturally important species.

Outcome: robust performancee0
Qualifier: both model plants and agriculturally important speciese0

Not reportedValue

Verbatim

e0
“demonstrated robust performance across both model plants and agriculturally important species”
✓ located verbatim in the source · abstract, characters 1154–1247

Title

S ² ‐ PepAnalyst : A Web Tool for Predicting Plant Small Signalling Peptides

Kelly L. Vomo‐Donfack; Mariem Abaach; Ana M. Luna; et al. Plant Biotechnology Journal 2026. doi:10.1111/pbi.70536

Other claims from this paper (4)

Method / resource The SSP prediction tool achieved high predictive accuracy of 99.5% when validated against experimentally confirmed peptides, while maintaining low false-negative rates.
Method / resource The tool successfully classified peptides into functionally distinct families such as CLE and RALF.
Method / resource The tool identified non-canonical SSPs that lack traditional signal peptides.
Method / resource The tool is provided as a freely available web resource.

Method / resourceobservedoriginal resultbasis n/a

Claim · model-written summary

The tool is provided as a freely available web resource.

Outcome: freely available resourcee0

Not reportedValue

Verbatim

e0
“As a freely available resource”
✓ located verbatim in the source · abstract, characters 1249–1279

Title

S ² ‐ PepAnalyst : A Web Tool for Predicting Plant Small Signalling Peptides

Kelly L. Vomo‐Donfack; Mariem Abaach; Ana M. Luna; et al. Plant Biotechnology Journal 2026. doi:10.1111/pbi.70536

Other claims from this paper (4)

Method / resourceobservedoriginal resultmeasured

Claim · model-written summary

Alginate/chitosan nanoparticles loaded with atrazine reached an encapsulation efficiency above 50% at 30,000 rpm.

Outcome: encapsulation efficiency (EE)e0
Value: above 50%e0

Verbatim

e0
“showed an encapsulation efficiency (EE) of the active ingredient above 50% under the rotation speed of 30,000 rpm”
✓ located verbatim in the source · abstract, characters 452–565

Title

Preparation, Characterization of Alginate/Chitosan Nanocarriers for Atrazine Encapsulation and Its Pre‐ and Post‐Emergence Herbicidal Activity Against Bidens Pilosa L.

Guanmei Chen; Yuan Zhang; Yuan Wang; et al. Journal of Applied Polymer Science 2026. doi:10.1002/app.70472

Other claims from this paper (1)

Intervention effect Atrazine-loaded nanoparticles gave better weed control of Bidens pilosa than atrazine solutions.

Method / resourceobservedoriginal resultpositivebasis n/a

Claim · model-written summary

A yield dataset at 10 m resolution was produced for major maize, rice and soybean regions of Northeast China for 2016 to 2021, with a method that estimates yields without field measurements.

Taxon: maizeZea mayst0
Outcome: dataset with 10 m resolutione0
Value: 10 me0
Qualifier: without field measurementse1
Setting: Northeast Chinae0

Verbatim

e0
“producing a detailed dataset with 10 m resolution, covering major regions of maize, rice, and soybean in Northeast China from 2016 to 2021”
✓ located verbatim in the source · abstract, characters 406–544

e1
“an innovative method for estimating crop yields without field measurements”
✓ located verbatim in the source · abstract, characters 702–776

t0
“maize”
✓ located verbatim in the source · title, characters 7–12

Title

A 10 m maize, rice and soybean yield dataset from 2016 to 2021 in Northeast China

Fei Teng; Minglei Wang; Wenjiao Shi; et al. Scientific Data 2026. doi:10.1038/s41597-026-06719-0

Other claims from this paper (9)

Method / resource Validation of the yield model for maize gave r values of 0.39.
Method / resource Validation of the yield model for maize gave MREs of 12.14%.
Method / resource Validation of the yield model for maize gave rRMSEs of 16.97%.
Method / resource Validation of the yield model for rice gave r values of 0.51.
Method / resource Validation of the yield model for rice gave MREs of 11.96%.
Method / resource Validation of the yield model for rice gave rRMSEs of 16.12%.
Method / resource Validation of the yield model for soybean gave r values of 0.52.
Method / resource Validation of the yield model for soybean gave MREs of 14.06%.
Method / resource Validation of the yield model for soybean gave rRMSEs of 17.26%.

Method / resourceobservedoriginal resultmeasured

Claim · model-written summary

Validation of the yield model for maize gave r values of 0.39.

Taxon: maizeZea mayst0
Outcome: r valuese0
Value: 0.39e0

Verbatim

e0
“r values of 0.39, 0.51, and 0.52”
✓ located verbatim in the source · abstract, characters 857–889

t0
“maize”
✓ located verbatim in the source · title, characters 7–12

Title

A 10 m maize, rice and soybean yield dataset from 2016 to 2021 in Northeast China

Fei Teng; Minglei Wang; Wenjiao Shi; et al. Scientific Data 2026. doi:10.1038/s41597-026-06719-0

Other claims from this paper (9)

Method / resource A yield dataset at 10 m resolution was produced for major maize, rice and soybean regions of Northeast China for 2016 to 2021, with a method that estimates yields without field measurements.
Method / resource Validation of the yield model for maize gave MREs of 12.14%.
Method / resource Validation of the yield model for maize gave rRMSEs of 16.97%.
Method / resource Validation of the yield model for rice gave r values of 0.51.
Method / resource Validation of the yield model for rice gave MREs of 11.96%.
Method / resource Validation of the yield model for rice gave rRMSEs of 16.12%.
Method / resource Validation of the yield model for soybean gave r values of 0.52.
Method / resource Validation of the yield model for soybean gave MREs of 14.06%.
Method / resource Validation of the yield model for soybean gave rRMSEs of 17.26%.

Method / resourceobservedoriginal resultmeasured

Claim · model-written summary

Validation of the yield model for maize gave MREs of 12.14%.

Taxon: maizeZea mayst0
Outcome: MREse0
Value: 12.14%e0

Verbatim

e0
“MREs of 12.14%, 11.96%, and 14.06%”
✓ located verbatim in the source · abstract, characters 891–925

t0
“maize”
✓ located verbatim in the source · title, characters 7–12

Title

A 10 m maize, rice and soybean yield dataset from 2016 to 2021 in Northeast China

Fei Teng; Minglei Wang; Wenjiao Shi; et al. Scientific Data 2026. doi:10.1038/s41597-026-06719-0

Other claims from this paper (9)

Method / resource A yield dataset at 10 m resolution was produced for major maize, rice and soybean regions of Northeast China for 2016 to 2021, with a method that estimates yields without field measurements.
Method / resource Validation of the yield model for maize gave r values of 0.39.
Method / resource Validation of the yield model for maize gave rRMSEs of 16.97%.
Method / resource Validation of the yield model for rice gave r values of 0.51.
Method / resource Validation of the yield model for rice gave MREs of 11.96%.
Method / resource Validation of the yield model for rice gave rRMSEs of 16.12%.
Method / resource Validation of the yield model for soybean gave r values of 0.52.
Method / resource Validation of the yield model for soybean gave MREs of 14.06%.
Method / resource Validation of the yield model for soybean gave rRMSEs of 17.26%.

Method / resourceobservedoriginal resultmeasured

Claim · model-written summary

Validation of the yield model for maize gave rRMSEs of 16.97%.

Taxon: maizeZea mayst0
Outcome: rRMSEse0
Value: 16.97%e0

Verbatim

e0
“rRMSEs of 16.97%, 16.12%, and 17.26%”
✓ located verbatim in the source · abstract, characters 931–967

t0
“maize”
✓ located verbatim in the source · title, characters 7–12

Title

A 10 m maize, rice and soybean yield dataset from 2016 to 2021 in Northeast China

Fei Teng; Minglei Wang; Wenjiao Shi; et al. Scientific Data 2026. doi:10.1038/s41597-026-06719-0

Other claims from this paper (9)

Method / resource A yield dataset at 10 m resolution was produced for major maize, rice and soybean regions of Northeast China for 2016 to 2021, with a method that estimates yields without field measurements.
Method / resource Validation of the yield model for maize gave r values of 0.39.
Method / resource Validation of the yield model for maize gave MREs of 12.14%.
Method / resource Validation of the yield model for rice gave r values of 0.51.
Method / resource Validation of the yield model for rice gave MREs of 11.96%.
Method / resource Validation of the yield model for rice gave rRMSEs of 16.12%.
Method / resource Validation of the yield model for soybean gave r values of 0.52.
Method / resource Validation of the yield model for soybean gave MREs of 14.06%.
Method / resource Validation of the yield model for soybean gave rRMSEs of 17.26%.

Method / resourceobservedoriginal resultmeasured

Claim · model-written summary

Validation of the yield model for rice gave r values of 0.51.

Taxon: riceOryza sativat0
Outcome: r valuese0
Value: 0.51e0

Verbatim

e0
“r values of 0.39, 0.51, and 0.52”
✓ located verbatim in the source · abstract, characters 857–889

t0
“rice”
✓ located verbatim in the source · title, characters 14–18

Title

A 10 m maize, rice and soybean yield dataset from 2016 to 2021 in Northeast China

Fei Teng; Minglei Wang; Wenjiao Shi; et al. Scientific Data 2026. doi:10.1038/s41597-026-06719-0

Other claims from this paper (9)

Method / resource A yield dataset at 10 m resolution was produced for major maize, rice and soybean regions of Northeast China for 2016 to 2021, with a method that estimates yields without field measurements.
Method / resource Validation of the yield model for maize gave r values of 0.39.
Method / resource Validation of the yield model for maize gave MREs of 12.14%.
Method / resource Validation of the yield model for maize gave rRMSEs of 16.97%.
Method / resource Validation of the yield model for rice gave MREs of 11.96%.
Method / resource Validation of the yield model for rice gave rRMSEs of 16.12%.
Method / resource Validation of the yield model for soybean gave r values of 0.52.
Method / resource Validation of the yield model for soybean gave MREs of 14.06%.
Method / resource Validation of the yield model for soybean gave rRMSEs of 17.26%.

Method / resourceobservedoriginal resultmeasured

Claim · model-written summary

Validation of the yield model for rice gave MREs of 11.96%.

Taxon: riceOryza sativat0
Outcome: MREse0
Value: 11.96%e0

Verbatim

e0
“MREs of 12.14%, 11.96%, and 14.06%”
✓ located verbatim in the source · abstract, characters 891–925

t0
“rice”
✓ located verbatim in the source · title, characters 14–18

Title

A 10 m maize, rice and soybean yield dataset from 2016 to 2021 in Northeast China

Fei Teng; Minglei Wang; Wenjiao Shi; et al. Scientific Data 2026. doi:10.1038/s41597-026-06719-0

Other claims from this paper (9)

Method / resource A yield dataset at 10 m resolution was produced for major maize, rice and soybean regions of Northeast China for 2016 to 2021, with a method that estimates yields without field measurements.
Method / resource Validation of the yield model for maize gave r values of 0.39.
Method / resource Validation of the yield model for maize gave MREs of 12.14%.
Method / resource Validation of the yield model for maize gave rRMSEs of 16.97%.
Method / resource Validation of the yield model for rice gave r values of 0.51.
Method / resource Validation of the yield model for rice gave rRMSEs of 16.12%.
Method / resource Validation of the yield model for soybean gave r values of 0.52.
Method / resource Validation of the yield model for soybean gave MREs of 14.06%.
Method / resource Validation of the yield model for soybean gave rRMSEs of 17.26%.

Method / resource 684,532 claims

684,532 claims