Model error propagation in a compatible tree volume, biomass, and carbon prediction system by James A. Westfall, Philip J. Radtke, David M. Walker, John W. Coulston

“…However, the propagation of model error can be a concern as this compatibility often relies on predictions for one attribute providing the basis for other attributes. …”
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Correcting for measurement errors in a long‐term aerial survey with auxiliary photographic data by Jamie L. Brusa, Matthew T. Farr, Joseph Evenson, Emily Silverman, Bryan Murphie, Thomas A. Cyra, Heather J. Tschaekofske, Kyle A. Spragens, Sarah J. Converse

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Investigation of an Optimized Linear Regression Model with Nonlinear Error Compensation for Tool Wear Prediction by Lihua Shen, Baorui Du, He Fan, Hailong Yang

“…Firstly, a linear prediction benchmark model is constructed: Support Vector Regression (SVR) is used to preliminarily model the tool wear process, obtaining initial prediction results and their error distribution. Building on this foundation, an Autoencoder (AE) is introduced to establish a nonlinear mapping relationship for the errors, achieving effective compensation of the SVR prediction results and establishing the SVR–AE prediction model. …”
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Exploring the Effects of Multi-Factors on User Emotions in Scenarios of Interaction Errors in Human–Robot Interaction by Wa Gao, Yuan Tian, Shiyi Shen, Yang Ji, Ning Sun, Wei Song, Wanli Zhai

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“Ensembled transfer learning approach for error reduction in landslide susceptibility mapping of the data scare region” by Ankit Singh, Nitesh Dhiman, K. C. Niraj, Dericks Praise Shukla

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Propagating observation errors to enable scalable and rigorous enumeration of plant population abundance with aerial imagery by Andrii Zaiats, T. Trevor Caughlin, Jennyffer Cruz, David S. Pilliod, Megan E. Cattau, Rongsong Liu, Richard Rachman, Maisha Maliha, Donna Delparte, John D. J. Clare

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Winter Wheat Yield Prediction and Influencing Factors Analysis Based on FourierGNN–Random Forest Combined Modeling by Jianqin Ma, Yijian Chen, Bifeng Cui, Yu Ding, Xiuping Hao, Yan Zhao, Junsheng Li, Xianrui Su

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A Mean Weighted Squared Error-based Neural Classifier for Intelligent Pattern Recognition in Smart Grids by Mehdi Khashei, Mehrnaz Ahmadi, Fatemeh Chahkoutahi

“…This paper introduces a new extension of the conventional Mean Squared Error loss function, called Mean Weighted Squared Error (MWSE), specifically designed for renewable energy classification purposes. …”
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Analysis and reduction of topographic effect induced errors in land surface temperature retrieval over the Tibetan Plateau by Yuejie Zhang, Qinghong Sheng, Kerui Li, Bo Wang, Jun Li, Xiao Ling, Fan Gao

“…Random forest (RF) models were employed to assess the contribution of topographic effects to these errors. …”
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How much timber from Norway spruce (Picea abies L.) is left on the harvesting site as treetops? A study on scaling and grading yields of harvesters by Jiří Dvořák, Marcel Štolc, Martin Jankovský, Pavel Natov, Arkadiusz Stańczykiewicz, Michal Allman, Andrej Liška

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Square Root Compression and Noise Effects in Digitally Transformed Images by Craig E. DeForest, Chris Lowder, Daniel B. Seaton, Matthew J. West

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Exploring the Link: A Systematic Review and Meta‐Analysis on the Prevalence and Association Between Refractive Errors and Intermittent Exotropia by Najah K. Mohammad, Ibrahim Ali Rajab, Mohammed Tareq Mutar, Mustafa Ismail

“…ABSTRACT Background and Aims Refractive errors and intermittent exotropia are prevalent conditions in pediatric populations, impacting visual development and quality of life. …”
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Predicting onset of myopic refractive error in children using machine learning on routine pediatric eye examinations only by Yonina Ron, Tchelet Ron, Naomi Fridman, Anat Goldstein

“…This study introduces an AI-based method to identify children at higher risk for the onset of myopic refractive error, enabling personalized follow-up and treatment plans. …”
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Estimating ages of white‐tailed deer: Age and sex patterns of error using tooth wear‐and‐replacement and consistency of cementum annuli by Daniel J. Storm, Michael D. Samuel, Robert E. Rolley, Thomas Beissel, Bryan J. Richards, Timothy R. Van Deelen

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Static evaluation of a virtual fence collar: precision and accuracy relative to a conventional GNSS collar across habitats by Edward W. Bork, Sydney G. Lopes, Alexandra J. Harland, Matthew J. Francis, Carolyn J. Fitzsimmons, Cameron N. Carlyle, Francisco J. Novais

“…This study compared the precision and accuracy errors of a conventional GNSS collar intended primarily for geolocation (Lotek LiteTrack 420) to that of a virtual fencing (VF) collar (Nofence), including the effect of different habitats across the landscape. …”
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Diagnosing aortic dissection: A review of this elusive, lethal diagnosis by Blake Briggs, David Cline

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Predicting errors in accident hotspots and investigating satiotemporal, weather, and behavioral factors using interpretable machine learning: An analysis of telematics big data. by Ali Golestani, Nazila Rezaei, Mohammad-Reza Malekpour, Naser Ahmadi, Seyed Mohammad-Navid Ataei, Sepehr Khosravi, Ayyoob Jafari, Saeid Shahraz, Farshad Farzadfar

“…Merging this data with a weather-related dataset resulted in a comprehensive dataset containing location, time, weather, and error type variables. After preprocessing, 619,988 records without any missing values were used to train and compare the performance of six machine learning models including logistic regression, K-nearest neighbors, random forest, Extreme Gradient Boosting (XGBoost), Naïve Bayes, and support vector machine. …”
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Quantifying observer variance in expansive monitoring program indicator data with heterogeneous-variance mixed-effects models by Leah T. Dreesmann, Timothy R. Johnson, Jason W. Karl

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Counting animals in orthomosaics from aerial imagery: Challenges and future directions by Ismael V. Brack, Camila Ferrara, German Forero‐Medina, Enrique Domic‐Rivadeneira, Omar Torrico, Kevin Thomas Wanovich, Benjamin Wilkinson, Denis Valle

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Pharmacist-led surgical medicines prescription optimization and prediction service improves patient outcomes - a machine learning based study by Xianlin Li, Xianlin Li, Xiunan Yue, Lan Zhang, Xiaojun Zheng, Nan Shang

“…The Random Forest (RF) model performed the best (AUC = 0.893) and retained high accuracy with 12 features (AUC = 0.886). …”
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