Carbon stock in calophyllum inophyllum provenances from eight islands in Indonesia: associate soil physicochemical properties and litter fiber content by Tri Maria Hasnah, Eko Hanudin, Budi Leksono, Syed Ajijur Rahman, Benito Heru Purwanto, Eritrina Windyarini, Agus Muhamad Maulana, Himlal Baral

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Point-of-care diagnostics and resistance phenotyping to combat ash dieback by Pierluigi Bonello, Anna O. Conrad, Dušan Sadiković, Mateusz Liziniewicz, Michelle Cleary

“…Here, we show that presymptomatic infected trees can be distinguished from pathogen-free trees with a testing error rate of 0.161 in a controlled inoculation experiment. …”
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AGB carbon stock analysis in the Indigenous agroforestry of the Ecuadorian Amazon: Chakra and Aja as Natural Climate Solutions by Paulina Álava-Núñez, Bolier Torres, Miguel Castro, Marco Robles

“…This sampling was implemented with a 95% confidence interval and a 10% error margin. Additionally, two other land uses (primary forest and an expert-identified best agroforestry - Model Chakra) were included, although they were not statistically defined. …”
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Use of Machine Learning to Predict California Bearing Ratio of Soils by Semachew Molla Kassa, Betelhem Zewdu Wubineh

“…From these evaluation metrics, the random forest algorithm gets a smaller error and larger relative error (R2) value to compare with other algorithms. …”
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Evaluation of snow parameters at weather stations in small catchments in the south of Western Siberia by D. K. Pershin, L. F. Lubenets, D. V. Chernykh

“…The small snow depth error occurred due to the composition of the error distribution and large differences between open and forested areas.…”
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The Development of Digital Twin Baby Incubators for Fault Detection and Performance Analysis by Hatice Kabaoğlu, Emine Uçar, Fecir Duran

“…The digital twin employs a hybrid model integrating Long Short-Term Memory (LSTM) and Random Forest (RF) algorithms to predict potential errors and alarms. …”
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Advanced hybrid machine learning based modeling for prediction of properties of ionic liquids at different temperatures by Saud Bawazeer

“…Considering the MAPE error rate, DT, ET, and RF have errors of 4.59E-02, 2.05E-02, and 2.59E-02, respectively. …”
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Measuring the dynamic wind load acting on standing trees in the field without destroying them. by Satoru Suzuki, Ayana Miyashita

“…Wind loads are a factor in tree growth, tree architecture, and the occurrence of disasters and forest disturbances, e.g., tree falls. To understand forest ecosystems and manage forests effectively, it is necessary to understand the relationship between wind loads and trees. …”
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Evaluating LiDAR technology for accurate measurement of tree metrics and carbon sequestration by Suradet Tantrairatn, Auraluck Pichitkul, Nutchanan Petcharat, Pawarut Karaked, Atthaphon Ariyarit

“…The method is as follows: • Three measurement methods were compared: conventional techniques using diameter tape and hypsometers, manual LiDAR measurements, and automated measurements using 3D Forest Inventory software with the CloudCompare plugin. • The Mean Absolute Percentage Error (MAPE) for carbon sequestration was 4.276 % for manual LiDAR measurements and 6.901 % for the 3D Forest Inventory method. • Root Mean Square Error (RMSE) values for carbon sequestration using LiDAR measurements were 33.492 kgCO2e, whereas RMSE values for the 3D Forest Inventory method were significantly higher. …”
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Enhancing safety in surface mine blasting operations with IoT based ground vibration monitoring and prediction system integrated with machine learning by Mangalpady Aruna, Harsha Vardhan, Abhishek Kumar Tripathi, Satyajeet Parida, N. V. Raja Sekhar Reddy, Krishna Moorthy Sivalingam, Li Yingqiu, P. V. Elumalai

“…Among these, the Random Forest model outperformed the others, achieving an R2 score of 0.92, Mean Absolute Error (MAE) of 0.21, and Root Mean Squared Error (RMSE) of 0.31. …”
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Real-Time Estimation of Near-Surface Air Temperature over Greece Using Machine Learning Methods and LSA SAF Satellite Products by Athanasios Karagiannidis, George Kyros, Konstantinos Lagouvardos, Vassiliki Kotroni

“…The mean absolute error (MAE) of the NSAT estimation model was 0.96 °C, while the mean biased error (MBE) was −0.01 °C and the R² was 0.976. …”
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Dementia Classification Based on Magnetic Resonance Scans Comparing Traditional and Modern Machine Learning Models’ Quintessence by Andreea POPOVICIU, Diogen BABUC, Todor IVAŞCU

“…The performance evaluation of each model was based on metrics such as accuracy, sensitivity, specificity, and statistical errors (determination coefficient, mean squared error, root mean squared error). …”
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Erratum

“…The journal’s editors and the authors apologize to readers for the errors in two articles published in “Zeszyty Teoretyczne Rachunkowości” in 2024.In the article:Beata Sadowska, Relevant information from the perspective of sustainable forest management – auditing socio-environmental information and data,published in “Zeszyty Teoretyczne Rachunkowości” 2024, Vol. 48, No. 3, pp. 233–262, https://doi.org/10.5604/01.3001.0054.7265The error occurs on page 236:For example, Marshall acknowledged the problem of imperfect information, although it was not of great academic interest at the time.Reason for erratum:The original sentence omitted the necessary citation to Mielcarek (2011, p. 73).It should be:For example, A. …”
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Everyone Knows Who is Stupid Around Here by Sinan Cem Kızıl, Bengisu Derebaşı

“…To specify what architectural stupidity is, we must acknowledge that not all failures of architecture are ‘errors’, some are worse. This article discusses the already architecturally situated concept of error and distinguishes it from stupidity in terms of ‘technicities’ that fail. …”
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AI-Driven Dynamic Covariance for ROS 2 Mobile Robot Localization by Bogdan Felician Abaza

“…Aggressive dynamics posed challenges, with errors up to 0.9491 rad due to data distribution bias and Random Forest model constraints. …”
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Burned area detection based on time-series analysis in a cloud computing environment by J.A. Anaya, W.F. Sione, A.M. Rodriguez-Montellano

“…There are large omission errors in the estimation of burned area in map products that are generated at a global scale. …”
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Retrieval of cloud fraction using machine learning algorithms based on FY-4A AGRI observations by J. Xia, L. Guan

“…Both RF and MLP models performed well in cloud fraction retrieval, showing lower mean error (ME), mean absolute error (MAE) and root mean square error (RMSE) compared to operational products. …”
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A traceability model for upper corner gas in fully mechanized mining faces based on XGBoost-SHAP by SHENG Wu, WANG Lingzi

“…Case analysis results showed that: ① the coefficient of determination (R2), mean absolute error (MAE), and root mean square error (RMSE) of the XGBoost model were 0.93, 0.007, and 0.008, respectively, indicating the highest goodness of fit and the lowest errors compared with random forest (RF), support vector regression (SVR), and gradient boosting decision tree (GBDT). ② The mean relative error of the XGBoost model was 4.478%, demonstrating higher accuracy and better generalization performance compared with the other models. ③ Based on the mean absolute SHAP values of input features, the gas concentration at T1 on the working face had the greatest influence on the gas concentration in the upper corner, followed by the gas concentration in the upper corner extraction pipeline, with the gas content and roof pressure of the mining coal seam following closely. …”
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Optimizing concrete strength: How nanomaterials and AI redefine mix design by Dan Huang, Guangshuai Han, Ziyang Tang

“…Model performances were evaluated using metrics including Root Mean Square Errors (RMSE), Mean Absolute Error (MAE), R-squared (R2), Normalized Mean Bias Error (NMBE), and Mean Absolute Percentage Error (MAPE). …”
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Multi-Source DEM Vertical Accuracy Evaluation of Taklimakan Desert Hinterland Based on ICESat-2 ATL08 and UAV Data by Mingyu Wang, Huoqing Li, Yongqiang Liu, Haojuan Li

“…While slope aspect has a relatively minor impact on errors, certain DEMs exhibit error variations in the SE and NW directions. …”
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