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“relative errors”

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Method / resourceobservedoriginal resultpositivemeasured

Claim · model-written summary

Mean relative model errors were below 7%.

Outcome: mean relative errorse0
Direction: less thane0
Value: less than 7%e0

Not reportedTaxon

Verbatim

e0
“mean relative errors less than 7%”
✓ located verbatim in the source · abstract, characters 871–904

Title

Factor de la reflectancia bi-cónica en especies vegetales contrastantes: modelación global

Fernando Paz Pellat; Alejandro Cano; Martín Bolaños; et al. Terra Latinoamericana 2018. doi:10.28940/terra.v36i1.222

Other claims from this paper (2)

Method / resource Global model fits to bi-conical reflectance were excellent, generally R2 at least 0.99.
Method / resource Multi-angular spectral information still had trouble discriminating species under generalized conditions.

Method / resourceobservedoriginal resultpositivemeasured

Claim · model-written summary

Models had mean relative errors close to 10%.

Taxon: tomatoSolanum lycopersicumt0
Outcome: mean relative errorse0
Value: close to 10%e0

Verbatim

e0
“mean relative errors close to 10%”
✓ located verbatim in the source · abstract, characters 1472–1505

t0
“tomato”
✓ located verbatim in the source · title, characters 20–26

Title

Novel estimation of tomato soluble solids content using linearly transformed reflectance-based spectral indices

Naisen Liu; Yuan Fang; Yongzhen Zhao; et al. Frontiers in Plant Science 2026. doi:10.3389/fpls.2026.1729375

Other claims from this paper (6)

Method / resource GA optimized indices outperformed conventional two-band indices.
Method / resource GA optimized indices outperformed full-spectrum partial least squares models.
Method / resource GA optimized indices were less sensitive to tomato type.
Method / resource Models achieved approximately 0.80 coefficients of determination.
Method / resource Models had root mean square errors around 0.6°Brix.
Method / resource Joint optimization improved model robustness and transferability.

Method / resourceobservedoriginal resultpositivemeasured

Claim · model-written summary

The coupled predictive equation achieved relative errors below 5%.

Taxon: Chimonobambusa utilisChimonobambusa utilist0
Outcome: relative errorse7
Value: below 5%e7

Verbatim

t0
“Chimonobambusa utilis”
✓ located verbatim in the source · title, characters 90–111

e7
“achieving relative errors below 5%”
✓ located verbatim in the source · abstract, characters 1311–1345

Title

Effect of Specimen Dimensions and Strain Rate on the Longitudinal Compressive Strength of Chimonobambusa utilis

Xudan Wang; Meng Zhang; Chunnan Liu; et al. Materials 2025. doi:10.3390/ma18174013

Other claims from this paper (5)

Intervention effect Increasing specimen size reduced compressive strength of Chimonobambusa utilis.
Intervention effect Increasing strain rate from 10−4 to 10−2 s−1 increased strength of 15 × 15 × 5 mm specimens to 80.27 MPa.
Association Strain rate has a secondary but enhancing influence on strength, while specimen size predominantly governs strength.
Observation Increasing specimen size shifted failure modes from shear to buckling and splitting.
Method / resource The Type II Weibull model had higher predictive accuracy than the Type I variant.

Method / resourceobservedoriginal resultpositivemeasured

Claim · model-written summary

Validation relative errors for the cotton water content models were below 18%.

Taxon: cottonGossypium hirsutumt0
Outcome: relative errors (RE)e0
Direction: less thane0
Value: 18%e0

Verbatim

e0
“relative errors (RE) were less than 18%”
✓ located verbatim in the source · abstract, characters 1377–1416

t0
“cotton plant”
✓ located verbatim in the source · title, characters 13–25

Title

Retrieval of cotton plant water content by UAV-based vegetation supply water index (VSWI)

Shuobo Chen; Yinwen Chen; Junying Chen; et al. International Journal of Remote Sensing 2020. doi:10.1080/01431161.2020.1718234

Other claims from this paper (7)

Association VSWI_GI was significantly correlated with cotton leaf water content.
Association VSWI_MTCI2 was significantly correlated with cotton bud and boll water content.
Association Stalk water content was negatively correlated with VSWI_MTCI2 at −0.895.
Association Stalk water content was negatively correlated with VSWI_DATT2 at −0.895.
Method / resource Multivariate linear models gave the best retrieval of cotton leaf, stalk, and bud and boll water content.
Method / resource The cotton water content models had modelling and validation determination coefficients near or above 0.8.
Method / resource Validation RMSE for the cotton water content models was below 0.17.

Method / resourceobservedoriginal resultpositivemeasured

Claim · model-written summary

Eighty-three percent of sample points had relative errors below 18.5%.

Outcome: relative errorse0
Direction: belowe0
Value: 83%e0
Qualifier: below 18.5%e0

Not reportedTaxon

Verbatim

e0
“83% of sample points had relative errors below 18.5%”
✓ located verbatim in the source · abstract, characters 792–844

Title

A Monitoring Method for Agricultural Soil Moisture Using Wireless Sensors and the Biswas Model

Yuanzhen Zhang; Guofang Wang; Lingzhi Li; et al. Agriculture 2025. doi:10.3390/agriculture15030344

Other claims from this paper (4)

Method / resource Model predictions of soil moisture storage correlated strongly with measurements, with R2 above 0.94.
Method / resource At depths of 0–1.2 m, 90% of sample points had relative errors under 15%.
Method / resource The system tracked daily soil moisture dynamics during maize growth.
Method / resource Predicted evapotranspiration had relative errors under 10.25% during maize growth.

Method / resourceobservedoriginal resultmeasured

Claim · model-written summary

Most species biomass average relative errors were less than 10%.

Taxon: alpine meadowt0
Outcome: average relative errorse4
Direction: less thane4
Value: 10%e4

Verbatim

t0
“Alpine Meadow”
✓ located verbatim in the source · title, characters 40–53

e4
“most species biomass' average relative errors were less than 10%”
✓ located verbatim in the source · abstract, characters 1192–1256

Title

Estimation of Aboveground Biomass in an Alpine Meadow on the Eastern Qinghai-Tibetan Plateau

Zhang Diany Mountain Research 2014

Other claims from this paper (7)

Method / resource For Kobresia graminifolia, Elymus nutans and Allium sikkimense the best biomass estimation model was the power function equation.
Method / resource For the other species the best biomass estimation model was the linear equation.
Method / resource For Saussurea hieracioides the best biomass estimation model was the quadratic term equation.
Method / resource The correlation coefficients of species biomass between estimated and measured values were greater than 0.9.
Method / resource For the community total biomass the correlation coefficient between estimated and measured values was 0.965.
Method / resource The average relative error of the community total biomass estimate was 5.672%.
Method / resource The relative error of most quadrats was less than 10%.

Method / resourceobservedoriginal resultsimulated

Claim · model-written summary

Simulated wheat LAI had relative errors below ten percent.

Taxon: wheatTriticum aestivumt0
Outcome: relative errors of predicted LAIe0
Direction: less thane0
Value: less than ten percente0

Verbatim

e0
“The simulation results showed that the relative errors of predicted LAI were less than ten percent”
✓ located verbatim in the source · abstract, characters 308–406

t0
“wheat”
✓ located verbatim in the source · title, characters 33–38

Title

Simulation on leaf area index in wheat

Tiemei Liu; Weixing Cao; Weihong Luo; et al. Mailei zuowu xuebao 2001

Other claims from this paper (1)

Association A curvilinear relation was established between green leaf partitioning index and physiological development time.

Method / resourceobservedoriginal resultnegativemeasured

Claim · model-written summary

Only 13.8% of test images had relative errors below 2%.

Taxon: Helianthus annuusHelianthus annuust0
Outcome: relative errors below 2%e2
Value: 13.8%e2

Verbatim

t0
“Helianthus annuus”
✓ located verbatim in the source · title, characters 84–101

e2
“Only 13.8% of test images had relative errors below 2%”
✓ located verbatim in the source · abstract, characters 1095–1149

Title

Rapid Classification and Deep Learning-Based Development Estimation of the Seeds of Helianthus annuus

Fami A. Mume; Daniil S. Ulyanov; Temur R. Muratov; et al. Plants 2026. doi:10.3390/plants15131930

Other claims from this paper (5)

Method / resource The seed-count model achieved a mean absolute count error of 61.3 seeds per image on the test set.
Method / resource The seed-count model achieved a mean relative error of 12.0%.
Method / resource The seed-count model achieved an mAP50 of 0.18 at the locked confidence threshold of 0.15.
Method / resource Larger YOLO models and augmentation variants did not improve seed-count performance over the baseline model.
Recommendation Improved localization in dense regions and domain adaptation for fresh heads or field conditions are needed.

Method / resourceobservedoriginal resultmeasured

Claim · model-written summary

The maize soil-water model had average relative errors of 5.26% to 7.85%.

Outcome: average relative errorse0
Value: 5.26% to 7.85%e0

Not reportedTaxon

Verbatim

e0
“the average relative errors are 5.26% to 7.85%”
✓ located verbatim in the source · abstract, characters 306–352

Title

A Dynamic Simulation of Soil Water Transport Under Controlled Alternative Irrigation of Maize

Peng Xu 2010

Other claims from this paper (3)

Method / resource The maize soil-water model had mean square deviations of 0.029 cm3/cm3 to 0.038 against measurements.
Method / resource The model was described as highly precise across maize growth stages.
Method / resource The model can predict soil water movement for controlled alternative irrigation of maize.

Method / resourceobservedoriginal resultnegativemeasured

Claim · model-written summary

APSIM yield predictions were sensitive to water balance carryover errors, sometimes causing large relative yield errors.

Taxon: wheatTriticum aestivume0
Outcome: predicted relative yieldse1
Qualifier: in some seasons large errorse1
Comparator: water balancee1

Not reportedDirection

Verbatim

e0
“growth of a wheat crop”
✓ located verbatim in the source · abstract, characters 924–946

e1
“Yield predictions with APSIM were sensitive to carry-over errors in the water balance from one season to the next, so that in some seasons large errors occurred in the predicted relative yields.”
✓ located verbatim in the source · abstract, characters 1860–2054

Title

Modelling water, nitrogen, and crop yield for a long-term fallow management experiment

ME Probert; BA Keating; JP Thompson; et al. Australian Journal of Experimental Agriculture 1995. doi:10.1071/ea9950941

Other claims from this paper (7)

Method / resource Both models predicted declining soil organic matter under all treatments, consistent with observations.
Method / resource Both models predicted declining fallow nitrate mineralization and accumulation capacity over time, consistent with observations.
Method / resource CENTURY predicted relative yields across nitrogen treatments better than APSIM.
Method / resource CENTURY predicted absolute grain yield less satisfactorily than APSIM.
Method / resource CENTURY predicted soil water less satisfactorily than APSIM.
Method / resource CENTURY predicted drainage less satisfactorily than APSIM.
Method / resource Both models reproduced observations sufficiently to provide useful insights into cropping systems with depleted soil fertility.

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“relative errors”

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