By organism › Green plants › Asteraceae › Helianthus

Observationobservedoriginal resultpositivesimulated

Claim · model-written summary

Fog harvesting can supply a maximum of 28% of sunflower water requirements.

Taxon: sunflowersHelianthuse0
Treatment: fog harvestinge0
Outcome: water requirementse0
Value: 28%e0
Qualifier: maximum valuee0

Verbatim

e0
“fog harvesting can supply a maximum value of 28% for sunflowers and 34% for maize water requirements”
✓ located verbatim in the source · abstract, characters 1315–1415

e1
“Maize and sunflower are studied as strategic multipurpose crops”
✓ located verbatim in the source · abstract, characters 460–523

Title

Decentralized rooftop and container agriculture using greywater and fog harvesting: a feasible strategy for water–food–energy security in mediterranean urban environments

Behrouz Pirouz; Stefania Anna Palermo; Hana Javadi Nejad; et al. Frontiers in Environmental Science 2026. doi:10.3389/fenvs.2026.1725063

Other claims from this paper (5)

Observationobservedoriginal resultmeasured

Claim · model-written summary

Triaspis aequoris was the only parasitoid reared from Smicronyx spp. collected in cultivated sunflowers.

Taxon: sunflowersHelianthuse5
Interactor: Smicronyx spp.Smicronyxe5pest
Second interactor: Triaspis aequorise5biocontrol_agent
Outcome: the only parasitoid rearede5

Verbatim

e5
“Triaspis aequoris was the only parasitoid reared from Smicronyx spp. collected in cultivated sunflowers”
✓ located verbatim in the source · abstract, characters 1501–1604

Title

Emergence- and DNA-based detection methods show inconsistent effects of planting date on parasitism of red sunflower seed weevil, Smicronyx fulvus LeConte, by Triaspis aequoris Martin

Jeffrey Cluever; Phillip Shults; Angelia Hogness; et al. Biological Control 2026. doi:10.1016/j.biocontrol.2026.106056

Other claims from this paper (7)

Method / resourceobservedoriginal resultpositivemeasured

Claim · model-written summary

TabPFN and ML models showed comparable accuracy, both outperforming baseline approaches in sub-national yield forecasting.

Taxon: maize, soybeans and sunflowersGlycine max HelianthusZea mayse0
Outcome: accuracye2
Direction: outperforminge2
Qualifier: comparablee2
Comparator: baseline approachese2
Setting: South Africae0

Verbatim

e0
“we present an application of a foundation model for small- to medium-sized tabular data (TabPFN), to sub-national yield forecasting task in South Africa for maize, soybeans and sunflowers”
✓ located verbatim in the source · abstract, characters 582–769

e2
“Results showed that TabPFN and ML models exhibit comparable accuracy, both outperforming the baseline approaches”
✓ located verbatim in the source · abstract, characters 1427–1539

Title

From rows to yields: how foundation models for tabular data simplify crop yield prediction

Filip Sabo; Michele Meroni; María Piles; et al. Scientific Reports 2026. doi:10.1038/s41598-026-50338-z

Other claims from this paper (6)

Method / resource The best-performing ML model achieved 6.8% rRMSEp for maize at the national level.
Method / resource TabPFN achieved 8.8% rRMSEp for maize at the national level, higher than the best-performing ML model.
Method / resource The best-performing ML model reached R² of 0.91 for maize at the national level.
Method / resource TabPFN reached R² of 0.86 for maize at the national level.
Method / resource TabPFN had faster tuning time than the six ML models it was benchmarked against.
Method / resource TabPFN required reduced feature engineering compared with the six ML models it was benchmarked against.

Method / resourceobservedoriginal resultpositivemeasured

Claim · model-written summary

TabPFN had faster tuning time than the six ML models it was benchmarked against.

Taxon: maize, soybeans and sunflowersGlycine max HelianthusZea mayse0
Outcome: tuning timee4
Direction: fastere4
Comparator: six ML modelse1

Verbatim

e0
“we present an application of a foundation model for small- to medium-sized tabular data (TabPFN), to sub-national yield forecasting task in South Africa for maize, soybeans and sunflowers”
✓ located verbatim in the source · abstract, characters 582–769

e1
“We benchmarked the results of the TabPFN against six ML models and two baseline models”
✓ located verbatim in the source · abstract, characters 1189–1275

e4
“Nonetheless, TabPFN demonstrated practical utility due to its faster tuning time and reduced requirement for feature engineering”
✓ located verbatim in the source · abstract, characters 1692–1820

Title

From rows to yields: how foundation models for tabular data simplify crop yield prediction

Filip Sabo; Michele Meroni; María Piles; et al. Scientific Reports 2026. doi:10.1038/s41598-026-50338-z

Other claims from this paper (6)

Method / resource TabPFN and ML models showed comparable accuracy, both outperforming baseline approaches in sub-national yield forecasting.
Method / resource The best-performing ML model achieved 6.8% rRMSEp for maize at the national level.
Method / resource TabPFN achieved 8.8% rRMSEp for maize at the national level, higher than the best-performing ML model.
Method / resource The best-performing ML model reached R² of 0.91 for maize at the national level.
Method / resource TabPFN reached R² of 0.86 for maize at the national level.
Method / resource TabPFN required reduced feature engineering compared with the six ML models it was benchmarked against.

Method / resourceobservedoriginal resultpositivemeasured

Claim · model-written summary

TabPFN required reduced feature engineering compared with the six ML models it was benchmarked against.

Taxon: maize, soybeans and sunflowersGlycine max HelianthusZea mayse0
Outcome: requirement for feature engineeringe4
Direction: reducede4
Comparator: six ML modelse1

Verbatim

e0
“we present an application of a foundation model for small- to medium-sized tabular data (TabPFN), to sub-national yield forecasting task in South Africa for maize, soybeans and sunflowers”
✓ located verbatim in the source · abstract, characters 582–769

e1
“We benchmarked the results of the TabPFN against six ML models and two baseline models”
✓ located verbatim in the source · abstract, characters 1189–1275

e4
“Nonetheless, TabPFN demonstrated practical utility due to its faster tuning time and reduced requirement for feature engineering”
✓ located verbatim in the source · abstract, characters 1692–1820

Title

From rows to yields: how foundation models for tabular data simplify crop yield prediction

Filip Sabo; Michele Meroni; María Piles; et al. Scientific Reports 2026. doi:10.1038/s41598-026-50338-z

Other claims from this paper (6)

Method / resource TabPFN and ML models showed comparable accuracy, both outperforming baseline approaches in sub-national yield forecasting.
Method / resource The best-performing ML model achieved 6.8% rRMSEp for maize at the national level.
Method / resource TabPFN achieved 8.8% rRMSEp for maize at the national level, higher than the best-performing ML model.
Method / resource The best-performing ML model reached R² of 0.91 for maize at the national level.
Method / resource TabPFN reached R² of 0.86 for maize at the national level.
Method / resource TabPFN had faster tuning time than the six ML models it was benchmarked against.

Observationobservedoriginal resultmeasured

Claim · model-written summary

Both sunflower species completed their cycles in most treatments, except at high concentrations of fireplace ash and oil burner dust.

Taxon: helianthusHelianthust0
Treatment: high concentrations of fireplace ash and oil burner duste1
Outcome: completed their cyclese1

Verbatim

t0
“Impact of solid waste on the growth of helianthus”
✓ located verbatim in the source · title, characters 0–49

e1
“Both sunflower species completed their cycles in most treatments, except at high concentrations of fireplace ash and oil burner dust”
✓ located verbatim in the source · abstract, characters 637–769

Title

Impact of solid waste on the growth of helianthus: potential for soil restoration and decontamination

S. K. Golfinopoulos; E. Chatzilaris; I. K. Kalavrouziotis; et al. International Journal of Environmental Science and Technology 2026. doi:10.1007/s13762-026-07090-8

Other claims from this paper (8)

Observationobservedoriginal resultpositivemeasured

Claim · model-written summary

Helianthus annuus showed higher resistance and growth rates.

Taxon: helianthusHelianthust0
Material: Helianthus annuuse2
Outcome: resistance and growth ratese2
Direction: highere2

Verbatim

t0
“Impact of solid waste on the growth of helianthus”
✓ located verbatim in the source · title, characters 0–49

e2
“Helianthus annuus exhibited higher resistance and growth rates”
✓ located verbatim in the source · abstract, characters 771–833

Title

Impact of solid waste on the growth of helianthus: potential for soil restoration and decontamination

S. K. Golfinopoulos; E. Chatzilaris; I. K. Kalavrouziotis; et al. International Journal of Environmental Science and Technology 2026. doi:10.1007/s13762-026-07090-8

Other claims from this paper (8)

Observationobservedoriginal resultmeasured

Claim · model-written summary

Effective phytoremediation threshold for fireplace ash is at or below 4.4 kg m-3.

Taxon: helianthusHelianthust0
Treatment: fireplace ashe3
Outcome: phytoremediation thresholdse3
Value: ≤ 4.4 kg m⁻ 3e3

Verbatim

t0
“Impact of solid waste on the growth of helianthus”
✓ located verbatim in the source · title, characters 0–49

e3
“effective phytoremediation thresholds established for fireplace ash (≤ 4.4 kg m⁻ 3 )”
✓ located verbatim in the source · abstract, characters 840–924

Title

Impact of solid waste on the growth of helianthus: potential for soil restoration and decontamination

S. K. Golfinopoulos; E. Chatzilaris; I. K. Kalavrouziotis; et al. International Journal of Environmental Science and Technology 2026. doi:10.1007/s13762-026-07090-8

Other claims from this paper (8)

Observationobservedoriginal resultmeasured

Claim · model-written summary

Effective phytoremediation threshold for oil burner dust is at or below 2.2 kg m-3.

Taxon: helianthusHelianthust0
Treatment: oil burner duste4
Outcome: phytoremediation thresholdse4
Value: ≤ 2.2 kg m⁻ 3e4

Verbatim

t0
“Impact of solid waste on the growth of helianthus”
✓ located verbatim in the source · title, characters 0–49

e4
“effective phytoremediation thresholds established for fireplace ash (≤ 4.4 kg m⁻ 3 ), oil burner dust (≤ 2.2 kg m⁻ 3 )”
✓ located verbatim in the source · abstract, characters 840–958

Title

Impact of solid waste on the growth of helianthus: potential for soil restoration and decontamination

S. K. Golfinopoulos; E. Chatzilaris; I. K. Kalavrouziotis; et al. International Journal of Environmental Science and Technology 2026. doi:10.1007/s13762-026-07090-8

Other claims from this paper (8)

Observationobservedoriginal resultmeasured

Claim · model-written summary

Effective phytoremediation threshold for forge dust is at or below 1.1 kg m-3.

Taxon: helianthusHelianthust0
Treatment: forge duste5
Outcome: phytoremediation thresholdse5
Value: ≤ 1.1 kg m⁻ 3e5

Verbatim

t0
“Impact of solid waste on the growth of helianthus”
✓ located verbatim in the source · title, characters 0–49

e5
“effective phytoremediation thresholds established for fireplace ash (≤ 4.4 kg m⁻ 3 ), oil burner dust (≤ 2.2 kg m⁻ 3 ), forge dust (≤ 1.1 kg m⁻ 3 )”
✓ located verbatim in the source · abstract, characters 840–987

Title

Impact of solid waste on the growth of helianthus: potential for soil restoration and decontamination

S. K. Golfinopoulos; E. Chatzilaris; I. K. Kalavrouziotis; et al. International Journal of Environmental Science and Technology 2026. doi:10.1007/s13762-026-07090-8

Other claims from this paper (8)

Helianthus 2,785 claims

2,785 claims