Method / resource 44,457 claims

Claim · model-written summary

A single-nucleus transcriptome atlas of peanut hypocotyls was built from nearly 29,915 nuclei and identified six major cell types.

Taxon

peanutArachis hypogaeae0

Organ / stage

hypocotylse0

Outcome

six major cell typese0

Value

29,915 nucleie0

Verbatim

e0

“we created a single nucleus transcriptome atlas for peanut hypocotyls by profiling nearly 29,915 nuclei identifying six major cell types”

Claim · model-written summary

Primary somatic embryogenesis is possible with 23 rubber tree clones but has limited propagation capacity.

Taxon

rubber treeHevea brasiliensise1

Outcome

Primary SE is possiblee1

Value

23 rubber tree clonese1

Qualifier

limited propagation capacitye1

Verbatim

e1

“Primary SE is possible with 23 rubber tree clones but has limited propagation capacity”

Claim · model-written summary

Landmark displacement measured with 3D Gaussian splatting agrees with manual recordings at R2 of at least 0.98.

Taxon

pome and drupe fruits of Rosaceaet0

Outcome

landmark displacemente0

Value

R 2 ≥ 0.98e0

Qualifier

high accuracye0

Comparator

manual recordingse0

Verbatim

t0

“pome and drupe fruits of Rosaceae”

e0

“the landmark displacement could be measured with high accuracy, with R 2 ≥ 0.98 when compared to manual recordings”

Claim · model-written summary

Genotype-specific developmental calendars were generated for seven Eragrostis curvula genotypes.

Taxon

Eragrostis curvulaEragrostis curvulat0

Outcome

developmental calendarse5

Value

seven E. curvula genotypese6

Verbatim

t0

“Eragrostis curvula”

e5

“to generate genotype-specific developmental calendars”

e6

“seven E. curvula genotypes with different ploidy levels (2X–7X)”

Claim · model-written summary

The authors created a repository of real leaves from eight experimental datasets to demonstrate the method.

Outcome

repository of real leavese1

Value

eight experimental datasetse1

Not reportedTaxon

Verbatim

e1

“we created a repository of real leaves gathered from eight experimental datasets”

Claim · model-written summary

Pseudo-landmarks with generalized Procrustes analysis compare all leaf shapes within and between species, using principal component analysis.

Outcome

compare all leaf shapese1

Comparator

both within and between speciese1

Not reportedTaxon

Verbatim

e1

“we explain how we can use pseudo‐landmarks to compare all leaf shapes, both within and between species, using dimension reduction techniques like principal component analysis”

Claim · model-written summary

Pseudo-landmarks with linear discriminant analysis can predict leaf shapes.

Outcome

predict leaf shapese1

Comparator

linear discriminant analysise1

Not reportedTaxon

Verbatim

e1

“can predict leaf shapes using pseudo‐landmarks through linear discriminant analysis”

Claim · model-written summary

The leaf shape analysis maps shape differences as leaves grew around a rosette, showing the transition of shape across development (phyllotaxy).

Outcome

transition of shape across developmente1

Comparator

as leaves grew around a rosettee1

Not reportedTaxon

Verbatim

e1

“maps differences in shape as leaves grew around a rosette, showing the transition of shape across development (phyllotaxy)”

Claim · model-written summary

Pre-training and fine-tuning gave the highest agreement with visual maturity ratings (R2 = 0.74 and 0.79).

Taxon

soybeanGlycine maxt0

Treatment

pre-training and fine-tuninge1

Outcome

agreement with visual ratingse1

Direction

higheste1

Value

R 2 = 0.74 and 0.79e1

Qualifier

trained on data from three prior years and tested on two independent trialse1

Comparator

single-source and multiple-source domain adaptatione2

Verbatim

t0

“predicting soybean maturity date”

e1

“When models were trained on data from three prior years and tested on two independent trials, the pre-training and fine-tuning technique demonstrated the best performance, with the highest agreement with visual ratings (coefficient of determination R 2 = 0.74 and 0.79) and root mean square errors of 1.70 and 1.96 days, respectively”

e2

“Transfer learning techniques, namely pre-training and fine-tuning, single-source and multiple-source domain adaptation, were evaluated using the multiple-year datasets”

Claim · model-written summary

Pre-training and fine-tuning gave lower root mean square errors (1.70 and 1.96 days).

Taxon

soybeanGlycine maxt0

Treatment

pre-training and fine-tuninge1

Outcome

root mean square errorse1

Direction

lowerinferrede1

Value

1.70 and 1.96 dayse1

Comparator

single-source and multiple-source domain adaptatione2

Verbatim

t0

“predicting soybean maturity date”

e1

“When models were trained on data from three prior years and tested on two independent trials, the pre-training and fine-tuning technique demonstrated the best performance, with the highest agreement with visual ratings (coefficient of determination R 2 = 0.74 and 0.79) and root mean square errors of 1.70 and 1.96 days, respectively”

e2

“Transfer learning techniques, namely pre-training and fine-tuning, single-source and multiple-source domain adaptation, were evaluated using the multiple-year datasets”