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“errors”

2,348 matches

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Method / resourceobservedoriginal resultmeasured

Claim · model-written summary

Country and crop forecasting errors were comparable to group-level errors.

Outcome: errorse0
Direction: comparablee0
Comparator: the ones obtained for the groupe0

Not reportedTaxon

Verbatim

e0
“the errors obtained are comparable to the ones obtained for the group.”
✓ located verbatim in the source · abstract, characters 1003–1073

Title

Global Cropland Yield Monitoring with Gaussian Processes

Maria Piles; Anna Mateo-Sanchis; Jordi Munoz-Mari; et al. 2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS 2021. doi:10.1109/igarss47720.2021.9554315

Other claims from this paper (3)

Method / resource Gaussian process models predicted crop yield with R2 from 0.5 to 0.95.
Method / resource Each country and crop model fit exceeded the data mean.
Recommendation The authors propose adopting the framework for national crop yield and production forecasts.

Observationobservedoriginal resultnegativemeasured

Claim · model-written summary

Additional errors were found, including errors affecting reported data and manuscript typographical errors.

Outcome: several otherse0

Not reportedTaxon

Verbatim

e0
“we found several others, some of which affect reported data and some of which are simple typographical errors introduced while preparing a manuscript”
✓ located verbatim in the source · abstract, characters 948–1097

Title

A pervasive typographical error in an equation for calculating nectar density is likely to have limited consequences

Hamish Symington; Joris Rockx Journal of Pollination Ecology 2025. doi:10.26786/1920-7603(2025)841

Other claims from this paper (3)

Method / resource The erroneous equation changes calculated nectar density by no more than 0.5%.
Method / resource The equation error is much smaller than variation in published nectar measurements.
Recommendation The authors recommend the more accurate equation for future studies.

Method / resourceobservedoriginal resultmeasured

Claim · model-written summary

Detection errors included more commission errors in upper stems.

Taxon: Scots pinePinus sylvestrist0
Organ / stage: upper stem regionse0
Outcome: commission errorse0
Direction: moree0

Not reportedValue

Verbatim

e0
“Error patterns revealed more commission errors in upper stem regions”
✓ located verbatim in the source · abstract, characters 1215–1283

t0
“Scots pine (Pinus sylvestris L.) trees”
✓ located verbatim in the source · abstract, characters 463–501

Title

Stem crook detection in terrestrial laser scanning data using semi-empirical simulations

Raul de Paula Pires; Nils Lindgren; Henrik J Persson; et al. Forestry An International Journal of Forest Research 2026. doi:10.1093/forestry/cpag026

Other claims from this paper (3)

Method / resource The detector achieved F1 above 0.6 at low IoU thresholds.
Method / resource The detector had mAP@50–95 of 0.24, with poorer localization at stricter IoU.
Method / resource Detection errors included more omissions near butt logs.

Associationobservedoriginal resultsimulated

Claim · model-written summary

Simulated leaf area index errors were associated with daily simulated ET errors, varying by growth stage.

Taxon: wheatTriticum aestivumt0
Organ / stage: growth stagee0
Outcome: errors in daily simulated ETe0
Qualifier: Errors in simulated leaf area indexe0

Not reportedDirection

Verbatim

e0
“Errors in simulated leaf area index were associated with errors in daily simulated ET, but the relationship varied with the growth stage”
✓ located verbatim in the source · abstract, characters 1671–1807

t0
“Wheat crop models”
✓ located verbatim in the source · title, characters 0–17

Title

Wheat crop models underestimate drought stress in semi-arid and Mediterranean environments

H. Webber; D. Cooke; C. Wang; et al. Field Crops Research 2025. doi:10.1016/j.fcr.2025.110032

Other claims from this paper (6)

Method / resource The model ensemble median underestimated wheat water use in all evaluated environments.
Association Relative underestimation of daily ET was constant across atmospheric evaporative demand levels.
Association Absolute ET error was greater on days of larger evaporative demand.
Method / resource Potential versus reference crop ET approach did not explain relative model performance.
Method / resource Atmospheric evaporative demand terms explained more seasonal water use uncertainty than soil depth or root growth terms.
Recommendation The authors recommend improving simulation of atmospheric ET demand.

Method / resourceobservedoriginal resultmeasured

Claim · model-written summary

For many whitefly detection models, most errors were species classification errors.

Taxon: tomatoSolanum lycopersicumt0
Outcome: most errors occurred in species classificatione0
Comparator: many modelse0

Verbatim

e0
“While, for many models, most errors occurred in species classification, those trained on low-resolution images had a higher proportion of localization errors.”
✓ located verbatim in the source · abstract, characters 853–1011

t0
“tomato leaflets”
✓ located verbatim in the source · title, characters 98–113

Title

Effect of image resolution on automatic detection of whitefly (Hemiptera: Aleyrodidae) species on tomato leaflets using deep learning

Mikio Kamei Smart Agricultural Technology 2023. doi:10.1016/j.atech.2023.100372

Other claims from this paper (7)

Method / resource Whitefly detection performed well with high-resolution training and testing images.
Method / resource Whitefly detection performance declined as image resolution decreased.
Method / resource Low-resolution whitefly models had a higher proportion of localization errors.
Method / resource Mismatch in resolution between training and inference produced poor whitefly detection performance.
Method / resource Mixed-resolution training gave similar whitefly detection performance to single-resolution training.
Method / resource detectoRS performed best for lower- or mixed-resolution whitefly images.
Method / resource YOLOX had the highest whitefly detection accuracy for high-resolution images.

Method / resourceobservedoriginal resultpositivemeasured

Claim · model-written summary

Prediction errors were low for the selected models.

Outcome: Prediction errorse0
Direction: lowe0

Not reportedComparatorValue

Verbatim

e0
“Prediction errors were low”
✓ located verbatim in the source · abstract, characters 1360–1386

Title

Prediction of organic acid concentration in BRS Capiaçu elephant grass silage

Isadora Osório Maciel Aguiar Freitas; Antonio Leandro Chaves Gurgel; Vitor Cardoso Queiroz; et al. Grassland Science 2025. doi:10.1111/grs.70019

Other claims from this paper (9)

Method / resource The best lactic acid model used dry matter alone and reached R2 0.98 and RMSPE 1.52.
Method / resource The best acetic acid model used pH and quadratic pH and buffering capacity terms, with R2 0.73 and RMSPE 0.98.
Method / resource The butyric acid model used crude protein and buffering capacity, with R2 0.51.
Method / resource The propionic acid model used organic matter, buffering capacity and quadratic soluble carbohydrate term, with R2 0.75.
Recommendation The authors consider the lactic acid model suitable for field use.
Recommendation The authors urge caution with the butyric acid model.
Method / resource Selected prediction models showed accuracy and precision, with correlation and agreement coefficients at least 0.70 for most models.
Method / resource Validation accepted the null hypothesis for all prediction models.
Method / resource The authors conclude silage organic acids can be estimated from pH and chemical composition.

Observationobservedoriginal resultnegativemeasured

Claim · model-written summary

Remaining classification errors clustered among morphologically similar varieties.

Taxon: Vitis viniferaVitis viniferae1
Outcome: errors concentrated among morphologically similar varietiese0

Not reportedDirection

Verbatim

e0
“remaining errors concentrated among morphologically similar varieties”
✓ located verbatim in the source · abstract, characters 1224–1293

e1
“Vitis vinifera variety identification”
✓ located verbatim in the source · abstract, characters 285–322

Title

vinum-Analytics

Nuno Ferreira; Filipe Pinto; António Valente; et al. Machine Learning and Knowledge Extraction 2026. doi:10.3390/make8040106

Other claims from this paper (11)

Method / resource The best vine-leaf detector achieved mAP@50–95 of 0.918.
Method / resource YOLO11n achieved about 27 FPS on CPU for cropping.
Method / resource The best YOLO11s classifier attained 94.06% Top-1 accuracy on 303 test images.
Method / resource The best YOLO11s classifier attained 93.92% macro-F1.
Method / resource The best YOLO11s classifier attained 100% Top-5 accuracy.
Method / resource Removing leaf detection reduced classification Top-1 accuracy from 75.00% to 68.75%.
Method / resource Manual and automatic crops differed by 2.44 percentage points on successfully detected images.
Method / resource Detection failures of 14.6% were the primary operational bottleneck.
Method / resource Repeated YOLO11s retraining showed stable Top-1 accuracy and macro-F1 across random seeds.
Method / resource YOLO11s was strongest overall on the field benchmark compared with competitive ResNet50 and EfficientNet-B0 baselines.
Recommendation The authors conclude that leaf detection and crop classification can support scalable identification in old vineyards.

Method / resourceobservedoriginal resultnegativemeasured

Claim · model-written summary

Partial occlusions commonly caused errors in blueberry detection models.

Taxon: blueberryt0
Outcome: errorse0
Qualifier: partial occlusionse0

Not reportedComparatorValue

Verbatim

e0
“partial occlusions commonly cause errors”
✓ located verbatim in the source · abstract, characters 762–802

t0
“Blueberry Detection”
✓ located verbatim in the source · title, characters 42–61

Title

Comprehensive Analysis of Model Errors in Blueberry Detection and Maturity Classification: Identifying Limitations and Proposing Future Improvements in Agricultural Monitoring

Cristhian A. Aguilera; Cristhian A. Aguilera; Carola Figueroa-Flores; et al. Agriculture 2023. doi:10.3390/agriculture14010018

Other claims from this paper (1)

Method / resource Optimization for embedded devices can increase model speed without losing precision.

Method / resourceobservedoriginal resultnegativemeasured

Claim · model-written summary

SSEB errors were 22%.

Taxon: banana crope1
Outcome: errorse0
Direction: greatere0
Value: 22%e0

Not reportedComparator

Verbatim

e0
“SSEB exhibited greater errors at 22%”
✓ located verbatim in the source · abstract, characters 923–959

e1
“banana crop”
✓ located verbatim in the source · abstract, characters 421–432

Title

Obtaining the daily actual evapotranspiration through remote sensing techniques application in Brazilian Semiarid

Camilla Kassar Borges; Rayonil Gomes Carneiro; Cleber Assis dos Santos; et al. Biblioteca Digital da Memória Científica do INPE (National Institute for Space Research) 2021. doi:10.29150/jhrs.v11.1.p18-31

Other claims from this paper (4)

Method / resource The algorithms correlated strongly with SEBAL, r greater than 0.93.
Method / resource S-SEBI errors were below 12% in orchards and caatinga.
Method / resource Both models had errors greater than 36% over bare soil relative to SEBAL.
Method / resource S-SEBI performed better than SSEB for ETa estimation, with lower deviations from SEBAL.

Method / resourceobservedoriginal resultpositivemeasured

Claim · model-written summary

Semantic anchoring constrained predictions under related errors.

Outcome: predictions under related errorse0
Comparator: semantic anchoringe0

Not reportedTaxon

Verbatim

e0
“semantic anchoring constrains predictions under related errors”
✓ located verbatim in the source · abstract, characters 890–952

Title

SMART: Structured multimodal agricultural retrieval-augmented transformer for plant disease diagnosis

Yuan-Chia Chan; Yao-Chung Fan; Hung-Chung Li; et al. Computers and Electronics in Agriculture 2026. doi:10.1016/j.compag.2026.111882

Other claims from this paper (8)

Method / resource Structured captions outperformed LLM-generated narratives for disease diagnosis.
Method / resource The vision-only baseline exceeded all LLM approaches on the 20-class pilot.
Method / resource Asymmetric loss design mitigated long-tailed degradation.
Method / resource The retrieval-based safety net bounded degradation under noisy inputs.
Method / resource Semantic anchoring benefited high-frequency classes.
Method / resource The retrieval-based safety net preferentially compensated rare classes.
Method / resource SMART achieved F1 of 0.9767 versus the best fine-tuned CNN F1 of 0.9118.
Method / resource SMART achieved F1 of 0.9767 versus the best multimodal foundation model F1 of 0.8904.

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