Prediction Intervals Research Articles

Surrogate endpoint meta-regression: Useful statistics for regulators and trialists

ObjectivesThe main purpose of using a surrogate endpoint is to estimate the treatment effect on the true endpoint sooner than with a true endpoint. Based on a meta-regression of historical randomized trials with surrogate and true endpoints, we discuss statistics for applying and evaluating surrogate endpoints. MethodsWe computed statistics from two types of linear meta-regressions for trial-level data: simple random effects and novel random effects with correlations among estimated treatment effects in trials with more than 2 arms. A key statistic is the estimated intercept of the meta-regression line. An intercept that is small or not statistically significant increases confidence when extrapolating to a new treatment because of consistency with a single causal pathway and invariance to labeling of treatments as controls. For a regulator applying the meta-regression to a new treatment, a useful statistic is the 95% prediction interval. For a clinical trialist planning a trial of a new treatment, useful statistics are the surrogate threshold effect proportion, the sample size multiplier adjusted for dropouts, and the novel true endpoint advantage. ResultsWe illustrate these statistics with surrogate endpoint meta-regressions involving anti-hypertension treatment, breast cancer screening, and colorectal cancer treatment. ConclusionRegulators and trialists should consider using these statistics when applying and evaluating surrogate endpoints.

The effects of SGLT2 inhibitors on right ventricle functions in heart failure patients: update meta-analysis of the current literature

Abstract Background There is some discrepancy in the present studies concerning the effect of sodium-glucose cotransporter type 2 (SGLT2) inhibitors on right ventricular (RV) functions in heart failure (HF) patients. The current meta-analysis was focused on determining the impact of SGLT2 inhibitors on RV functions in such individuals. Methods Two independent investigators searched PubMed, Google Scholar, and the Cochrane Library for articles of interest. To analyze heterogeneity, Higgins' I2 as well as prediction intervals and Egger's test were utilized to assess heterogeneity. The Newcastle-Ottawa standard ratings approach was used to assess the quality of observational studies. The Robin-I risk of bias algorithm was used to assess the bias risks of randomized studies. Results This meta-analysis evaluated 8 studies in total. Over the follow-up time frame, patients who used SGLT-2 inhibitors had substantially lower systolic pulmonary artery pressure (sPAP) and higher TAPSE values (mean difference (MD) = 1.30 [0.78;1.82], p =0.002 and MD =-5.39 [-7.88; -2.89], p=0.003, respectively). There was no significant difference in RVS’ values between follow-up and baseline (MD=1.38 [-1.35;3.80], p=0.169). However, as compared to the baseline period, fractional area contraction (FAC) values were substantially larger at the end of the follow-up period (MD=5.40 [3.74;7.07], p=0.015). Conclusion To the best of our knowledge, this is the first meta-analysis assessing the impact of SGLT2 inhibitors on RV function in HF patients. Our findings suggest that SGLT2 inhibitors may improve RV performance in HF patients.

Non-genetic risk factors for preeclampsia: an updated umbrella review

Abstract Background Preeclampsia (PE) is a common pregnancy disorder with complex mechanisms, and multiple risk factors may contribute to its development. Our objective is to update our literature search from our previous umbrella review, summarizing recent evidence on non-genetic risk factors associated with PE and assessing their credibility to identify those supported by robust epidemiological evidence. Methods We searched PubMed, EMBASE, Scopus, and ISI Web of Science up to June 2023. For each meta-analysis, we computed summary effect estimates, 95% confidence intervals, 95% prediction intervals, and assessed heterogeneity using I², while also examining small-study effects and excess significance bias. Quality assessment was conducted using the AMSTAR-2 tool. Results 89 meta-analyses were identified, providing data on 219 associations. Of these, 151 (69%) showed statistically significant findings at P < 0.05, with 52 (24%) remaining significant at P < 10-6. Large or very large heterogeneity was observed in 121 (55%) associations. Evidence for small-study effects and excess significance bias was found in 27 (12%) and 29 (13%) associations, respectively. Only 16 (7%) presented convincing evidence: Polycystic Ovary Syndrome (PCOS), PCOS (adjusted), Obstructive Sleep Apnea (OSA), Obesity (adjusted), Chronic Kidney Disease (CKD), Pregnancy Intrahepatic Cholestasis, SARS-CoV-2 infection (adjusted), Oocyte donation (OD) vs. In vitro fertilization (IVF), Fresh embryo transfer vs. spontaneous conception (SC) (singleton), Frozen embryo transfer vs. SC, OD vs. SC (singleton), IVF-OD vs. IVF-AO, IVF-OD fresh ET vs. IVF-AO fresh ET, NC frozen embryo transfer vs. AC frozen embryo transfer, Fetal sex (term), and Smoking. Conclusions Maternal health conditions like PCOS, along with reproductive interventions such as OD and IVF, as well as lifestyle factors like obesity and smoking, showed convincing associations with PE. Key messages • Maternal health conditions, reproductive interventions, and lifestyle factors showed compelling evidence of association with preeclampsia. • Our findings emphasize the multifactorial nature of preeclampsia and suggest potential avenues for further research and clinical intervention.

Prediction intervals and bands with improved coverage for functional data under noisy discrete observation

Prediction intervals and bands with improved coverage for functional data under noisy discrete observation

Symbol detection aided channel prediction in fast‐varying massive MIMO systems: Framework and performance analysis

AbstractThe channel in massive multiple‐input multiple‐output systems is fast‐varying that the pilot signal needs to be sent frequently. To obtain timely channel state information with less pilot overheads, a symbol detection aided channel prediction scheme is proposed in this paper. Then, the prediction error lower bound of the proposed scheme within one interval of effective prediction is analysed. Besides, the approximate close‐form post‐processing signal to noise ratio is derived for zero‐forcing detector with imperfect channel predictions. Numerical simulations are implemented to verify the validity of theoretical analysis. The results show that the theoretical expressions have a close match with the real simulated performance under various simulation parameter settings. In addition, the frequency of transmitting the pilot signals can be significantly reduced when adopting this proposed method. Moreover, the application of the proposed scheme can be further expanded when combining it with channel coding, thereby greatly improving the spectrum efficiency of the system.

Estimation of Tree Diameter at Breast Height from Aerial Photographs Using a Mask R-CNN and Bayesian Regression

A probabilistic estimation model for forest biomass using unmanned aerial vehicle (UAV) photography was developed. We utilized a machine-learning-based object detection algorithm, a mask region-based convolutional neural network (Mask R-CNN), to detect trees in aerial photographs. Subsequently, Bayesian regression was used to calibrate the model based on an allometric model using the estimated crown diameter (CD) obtained from aerial photographs and analyzed the diameter at breast height (DBH) data acquired through terrestrial laser scanning. The F1 score of the Mask R-CNN for individual tree detection was 0.927. Moreover, CD estimation using the Mask R-CNN was acceptable (rRMSE = 10.17%). Accordingly, the probabilistic DBH estimation model was successfully calibrated using Bayesian regression. A predictive distribution accurately predicted the validation data, with 98.6% and 56.7% of the data being within the 95% and 50% prediction intervals, respectively. Furthermore, the estimated uncertainty of the probabilistic model was more practical and reliable compared to traditional ordinary least squares (OLS). Our model can be applied to estimate forest biomass at the individual tree level. Particularly, the probabilistic approach of this study provides a benefit for risk assessments. Additionally, since the workflow is not interfered by the tree canopy, it can effectively estimate forest biomass in dense canopy conditions.

A Novel Method of Bridge Deflection Prediction Using Probabilistic Deep Learning and Measured Data

The deflection control of the main girder in suspension bridges, as flexible structures, is critically important during their operation. To predict the vertical deflection of existing suspension bridge girders under the combined effects of stochastic traffic loads and environmental temperature, this paper proposes an integrated deflection interval prediction method based on a Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), a probability density estimation layer, and bridge monitoring data. A time-series training dataset consisting of environmental temperature, vehicle load, and deflection monitoring data was built based on bridge health monitoring data. The CNN-LSTM combined layer is used to capture both local features and long-term dependencies in the time series. A Gaussian distribution (GD) is adopted as the probability density function, and its parameters are estimated using the maximum likelihood method, which outputs the optimal deflection prediction and probability intervals. Furthermore, this paper proposes a method for identifying abnormal deflections of the main girder in existing suspension bridges and establishes warning thresholds. This study indicates that, for short time scales, the CNN-LSTM-GD model achieves a 47.22% improvement in Root Mean Squared Error (RMSE) and a 12.37% increase in the coefficient of determination (R2) compared to the LSTM model. When compared to the CNN-LSTM model, it shows an improvement of 28.30% in RMSE and 6.55% in R2. For long time scales, the CNN-LSTM-GD model shows a 54.40% improvement in RMSE and a 10.22% increase in R2 compared to the LSTM model. Compared to the CNN-LSTM model, it improves RMSE by 38.43% and R2 by 5.31%. This model is instrumental in more accurately identifying abnormal deflections and determining deflection thresholds, making it applicable to bridge deflection early-warning systems.

Soil landscapes of the United States (SOLUS): Developing predictive soil property maps of the conterminous United States using hybrid training sets

AbstractDetailed soil property maps are increasingly important for land management decisions and environmental modeling. The US Soil Survey is investing in production of the Soil Landscapes of the United States (SOLUS), a new set of national predictive soil property maps. This paper documents initial 100‐m resolution maps of 20 soil properties that include various textural fractions, physical parameters, chemical parameters, carbon, and depth to restrictions. Many of these properties have not been previously mapped at this resolution. A hybrid training strategy helped increase training data by roughly 10‐fold over previous similar studies by combining commonly used laboratory data with underutilized field descriptions tied to soil survey map unit component property estimates (to help represent within polygon variability) as well as randomly selected soil survey map unit weighted average property estimates. Relative prediction intervals were used to help select which training data sources improved model performance. Conventional and spatial cross‐validation strategies yielded generally strong coefficients of determination between 0.5 and 0.7, but with substantial variability and outliers among the various properties, types of training data, and depths. Internal review of the maps highlighted both strengths and weaknesses of the maps, but most of the critical comments were in areas with high model uncertainty that can be used to guide future improvements. Generally, previously glaciated areas and complex large alluvial basins were harder to model. The new SOLUS 100‐m maps will be updated in the future to address identified issues and feedback as users interact with the data.

Fatigue Scattering Analytics and Prediction of SS 316L Fabricated by Laser Powder Bed Fusion

Abstract Laser powder bed fusion (LPBF) is an enabling process manufacture of complex metal components. However, LPBF is prone to generate geometrical defects (e.g., porosity, lack of fusion), which causes a significant fatigue scattering. However, LPBF fatigue scattering data and analysis in the literature are not only sparse and limited to tension-compression mode but also inconsistent. This article presents a robust high-frequency fatigue testing method to construct stress-cycle curves of SS 316L to understand the scattering nature and predict the scattering pattern. A series of bending fatigue tests are performed at different stress amplitudes. Two different runout criteria are used to investigate fatigue life, fatigue limits, and scattering. The endurance limit reaches around 300 MPa for the defect size distribution at the selected process space. The defect size-based fatigue limit model is found to underestimate the endurance limit by about 30 MPa when comparing with the experimental data. Fatigue scattering is further calculated by using 95% prediction intervals, showing that low fatigue scattering is present at high stresses while a large variation of fatigue life occurs at stresses near the knee point.

Enhancing late postmortem interval prediction: a pilot study integrating proteomics and machine learning to distinguish human bone remains over 15 years

BackgroundDetermining the postmortem interval (PMI) accurately remains a significant challenge in forensic sciences, especially for intervals greater than 5 years (late PMI). Traditional methods often fail due to the extensive degradation of soft tissues, necessitating reliance on bone material examinations. The precision in estimating PMIs diminishes with time, particularly for intervals between 1 and 5 years, dropping to about 50% accuracy. This study aims to address this issue by identifying key protein biomarkers through proteomics and machine learning, ultimately enhancing the accuracy of PMI estimation for intervals exceeding 15 years.MethodsProteomic analysis was conducted using LC–MS/MS on skeletal remains, specifically focusing on the tibia and ribs. Protein identification was performed using two strategies: a tryptic-specific search and a semitryptic search, the latter being particularly beneficial in cases of natural protein degradation. The Random Forest algorithm was used to model protein abundance data, enabling the prediction of PMI. A thorough screening process, combining importance scores and SHAP values, was employed to identify the most informative proteins for model’s training and accuracy.ResultsA minimal set of three biomarkers—K1C13, PGS1, and CO3A1—was identified, significantly improving the prediction accuracy between PMIs of 15 and 20 years. The model, based on protein abundance data from semitryptic peptides in tibia samples, achieved sustained 100% accuracy across 100 iterations. In contrast, non-supervised methods like PCA and MCA did not yield comparable results. Additionally, the use of semitryptic peptides outperformed tryptic peptides, particularly in tibia proteomes, suggesting their potential reliability in late PMI prediction.ConclusionsDespite limitations such as sample size and PMI range, this study demonstrates the feasibility of combining proteomics and machine learning for accurate late PMI predictions. Future research should focus on broader PMI ranges and various bone types to further refine and standardize forensic proteomic methodologies for PMI estimation.

An investigation of the relationship between long bone measurements and stature: Implications for estimating skeletal stature in subadults.

The present study introduces new regression formulae that address several challenges of current subadult stature estimation methods by 1) using a large, contemporary, cross-sectional sample of subadult skeletal remains; 2) generating regression models using both lengths and breadths; 3) utilizing both linear and nonlinear regression models to accommodate the nonlinear shape of long bone growth; and 4) providing usable prediction intervals for estimating stature. Eighteen long bone measurements, stature, and age were collected from computed tomography images for a sample of individuals (n = 990) between birth and 20 years from the United States. The bivariate relationship between long bone measurements and stature was modeled using linear and nonlinear methods on an 80% training sample and evaluated on a 20% testing sample. Equations were generated using pooled-sex samples. Goodness of fit was evaluated using Kolmogorov-Smirnov tests and mean absolute deviation (MAD). Accuracy and precision were quantified using percent testing accuracy and Bland-Altman plots. In total, 38 stature estimation equations were created and evaluated, all achieving testing accuracies greater than 90%. Nonlinear models generated better fits compared to linear counterparts and generally produced smaller MAD (3.65 - 15.90cm). Length models generally performed better than breadth models, and a mixture of linear and nonlinear methods resulted in highest testing accuracies. Model performance was not biased by sex, age, or measurement type. A freely available, online graphical user interface is provided for immediate use of the models by practitioners in forensic anthropology and will be expanded to include bioarchaeological contexts in the future.

A data-driven prediction for concrete crack propagation path based on deep learning method

Accurate and efficient prediction of concrete crack propagation path facilitates timely maintenance. To avoid errors arising from assumptions in physical models and parameter measurements, a data-driven prediction method for concrete crack propagation path is proposed. The spatio-temporal prediction approach is presented by unifying the conditional Generative Adversarial Network and Long Short-Term Memory Network (UcGLS-Net). The UcGLS-Net has the advantage of enabling temporal (by Long Short-Term Memory Network, LSTM) and spatial information (by the conditional Generative Adversarial Network, cGAN) in the concrete crack propagation process to be treated simultaneously and distinctly thus improving prediction accuracy. Notably, results demonstrate that UcGLS-Net still works in crack propagation path prediction even with unknown aggregate distribution. Moreover, the effects of historical input quantity and prediction intervals on prediction accuracy of the UcGLS-Net are studied and the network framework is optimized. Due to high efficiency, this study provides a new adaptable and real-time prediction method for concrete crack propagation path, offering support for the design, damage prediction, assessment, and maintenance of concrete structures.

SARIMA Model-Based Maximum Temperature Forecasting in Bangladesh: A Data-Driven Evaluation from 1981 to 2024

Bangladesh is a tropical nation where there are notable seasonal temperature changes. The Seasonal Autoregressive Integrated Moving Average (SARIMA) model is used in this study to forecast Bangladesh's maximum temperature from 2023 to 2042. The objective is to assess how rising temperatures can affect public health, energy consumption, and agriculture. Autocorrelation and partial autocorrelation analysis will be used to improve the model. Analysis was done using historical maximum temperature data spanning from 1981 to 2022. Forecasts were produced using the SARIMA model, whose parameters were chosen in accordance with plots of the autocorrelation function (ACF) and partial autocorrelation function (PACF). The model SARIMA (1,1,2)(0,0,1) is selected based on AIC. In order to account for forecast uncertainty, forecasts were created for the years 2023–2042. 95% prediction ranges were then calculated. Bangladesh's maximum temperatures are predicted by the SARIMA model to rise gradually, from roughly 33.75°C in 2023 to 34.17°C in 2042. With some degree of uncertainty, the 95% prediction intervals show a steady increasing trend between 33.53°C and 34.51°C. The anticipated increase in the highest temperatures has major consequences for Bangladesh. These results highlight how crucial it is to create adaptation plans and laws in order to lessen the effects of warming temperatures and increase resilience.

Introducing prediction intervals for sample means.

Researchers and practitioners are typically familiar with descriptive statistics and statistical inference. However, outside of regression techniques, little attention may be given to questions around prediction. In the current paper, we introduce prediction intervals using fundamental concepts that are learned in descriptive and inferential statistical training (i.e., sampling error, standard deviation). We walk through an example using simple hand calculations and reference a simple R package that can be used to calculate prediction intervals.

Vine Copula-Based Classifiers with Applications

AbstractThe vine pair-copula construction can be used to fit flexible non-Gaussian multivariate distributions to a mix of continuous and discrete variables. With multiple classes, fitting univariate distributions and a vine to each class lead to posterior probabilities over classes that can be used for discriminant analysis. This is more flexible than methods with the Gaussian and/or independence assumptions, such as quadratic discriminant analysis and naive Bayes. Some variable selection methods are studied to accompany the vine copula-based classifier because unimportant variables can make discrimination worse. Simple numerical performance metrics cannot give a full picture of how well a classifier is doing. We introduce categorical prediction intervals and other summary measures to assess the difficulty of discriminating classes. Through extensive experiments on real data, we demonstrate the superior performance of our approaches compared to traditional discriminant analysis methods and random forests when features have different dependent structures for different classes.

Inferring Surface NO2 Over Western Europe: A Machine Learning Approach With Uncertainty Quantification

AbstractNitrogen oxides (NOx = NO + NO2) are of great concern due to their impact on human health and the environment. In recent years, machine learning (ML) techniques have been widely used for surface NO2 estimation with rapid developments in computational power and big data. However, the uncertainties inherent to such retrievals are rarely studied. In this study, a novel ML framework has been developed, enhanced with uncertainty quantification techniques, to estimate surface NO2 and provide corresponding data‐induced uncertainty. We apply the Boosting Ensemble Conformal Quantile Estimator (BEnCQE) model to infer surface NO2 concentrations over Western Europe at the daily scale and 1 km spatial resolution from May 2018 to December 2021. High NO2 mainly appears in urban areas, industrial areas, and roads. The space‐based cross‐validation shows that our model achieves accurate point estimates (r = 0.8, R2 = 0.64, root mean square error = 8.08 μg/m3) and reliable prediction intervals (coverage probability, PI‐50%: 51.0%, PI‐90%: 90.5%). Also, the model result agrees with the Copernicus Atmosphere Monitoring Service (CAMS) model. The quantile regression in our model enables us to understand the importance of predictors for different NO2 level estimations. Additionally, the uncertainty information reveals the extra potential exceedance of the World Health Organization (WHO) 2021 limit in some locations, which is undetectable by only point estimates. Meanwhile, the uncertainty quantification allows assessment of the model's robustness outside existing in‐situ station measurements. It reveals challenges of NO2 estimation over urban and mountainous areas where NO2 is highly variable and heterogeneously distributed.

A Real-Time and Online Dynamic Reconfiguration against Cyber-Attacks to Enhance Security and Cost-Efficiency in Smart Power Microgrids Using Deep Learning

Ensuring the secure and cost-effective operation of smart power microgrids has become a significant concern for managers and operators due to the escalating damage caused by natural phenomena and cyber-attacks. This paper presents a novel framework focused on the dynamic reconfiguration of multi-microgrids to enhance system’s security index, including stability, reliability, and operation costs. The framework incorporates distributed generation (DG) to address cyber-attacks that can lead to line outages or generation failures within the network. Additionally, this work considers the uncertainties and accessibility factors of power networks through a modified point prediction method, which was previously overlooked. To achieve the secure and cost-effective operation of smart power multi-microgrids, an optimization framework is developed as a multi-objective problem, where the states of switches and DG serve as independent parameters, while the dependent parameters consist of the operation cost and techno-security indexes. The multi-objective problem employs deep learning (DL) techniques, specifically based on long short-term memory (LSTM) and prediction intervals, to effectively detect false data injection attacks (FDIAs) on advanced metering infrastructures (AMIs). By incorporating a modified point prediction method, LSTM-based deep learning, and consideration of technical indexes and FDIA cyber-attacks, this framework aims to advance the security and reliability of smart power multi-microgrids. The effectiveness of this method was validated on a network of 118 buses. The results of the proposed approach demonstrate remarkable improvements over PSO, MOGA, ICA, and HHO algorithms in both technical and economic indicators.

Maternal factors for neural tube defects in offspring: An umbrella review.

We conducted an umbrella review focusing on maternal risk factors during pregnancy associated with neural tube defects (NTDs). Our search was in databases PubMed, Scopus, and Web of Science. We specifically targeted meta-analyses examining maternal factors during pregnancy in relation to NTDs. The comparison involved assessing metrics such as odds ratio (OR) or related risk ratios reported in the included studies, as well as parameters like heterogeneity (I²), 95% prediction interval, small-study effects, excess significance biases, and sensitivity analysis. Three risk factors for fetal NTDs, namely hyperthermia with an OR of 1.92, obesity with an OR of 1.68, and passive smoking with an OR of 1.90, were classified as highly suggestive evidence (Class II). Influenza, with an OR of 3.33, was considered a risk factor with suggestive evidence (Class III). Multivitamin supplementation during pregnancy, with an OR of 0.76, and low maternal vitamin B12, with an OR of 2.41, were categorized as weak evidence (Class IV). We identified four risk factors including hyperthermia, influenza, obesity, and passive smoking as suggestive or highly suggestive evidence for NTDs. Low maternal vitamin B12 was identified as a risk factor for NTDs, supported by weak evidence.

Interval prediction method for dynamic zoning of displacements in super-high arch dam under significant water level fluctuations

Displacement is one of the most intuitive indicators reflecting the influence of internal and external environments on dam structures. However, existing displacement monitoring models mostly rely on static fitting of single point and deterministic point value estimation, failing to deeply consider the similarity and uncertainty of displacement in dynamic changes under significant water level fluctuations. To address this issue, the displacements are first divided into three periods: ascending, descending, and stable periods. Second, based on the Ward criterion, the displacements of measuring points in different periods are clustered and partitioned, and the CRITIC method is applied to amalgamate the displacement of multiple points within each partition into comprehensive displacements, which reflect the displacement changes of the corresponding partition. Then, the comprehensive displacements are predicted utilizing deep learning model convolutional neural network-gated recurrent unit (CNN-GRU). Finally, to consider the uncertainty and randomness of displacement changes, prediction intervals based on Bootstrap method are employed to quantify the impact of uncertain factors. Engineering examples show that the proposed method can provide scientific basis and technical support for the safety monitoring and health diagnosis of super-high arch dams.

Estimating and Modeling Pinus contorta Transpiration in a Montane Meadow Using Sap-Flow Measurements

This study quantifies the transpiration of encroached lodgepole pine (Pinus contorta var. murryana (Grev. & Balf.) Engelm.) in a montane meadow using pre-restoration sap-flow measurements. Lodgepole pine transpiration and its response to environmental variables were examined in Rock Creek Meadow (RCM), Southern Cascade Range, CA, USA. Sap-flow data from lodgepole pines were scaled to the meadow using tree survey data and then validated with MODIS evapotranspiration estimates for the 2019 and 2020 growing seasons. A modified Jarvis–Stewart model calibrated to 2020 sap-flow data analyzed lodgepole pine transpiration’s correlation with solar radiation, air temperature, vapor pressure deficit, and soil volumetric water content. Model validation utilized 2021 growing season sap-flow data. Calibration and validation employed a Markov Chain Monte Carlo (MCMC) approach through the DREAM(ZS) algorithm with a generalized likelihood (GL) function, enabling parameter and total uncertainty assessment. The model’s scaling was compared with simple scaling estimates. Average lodgepole pine transpiration at RCM ranged between 220.6 ± 25.3 and 393.4 ± 45.7 mm for the campaign (mid-July 2019 to mid-August 2020) and 100.2 ± 11.5 to 178.8 ± 20.7 mm for the 2020 partial growing season (April to mid-August), akin to MODIS ET. The model aligned well with observed normalized sap-velocity during the 2020 growing season (RMSE = 0.087). However, sap-velocity, on average, was underpredicted by the model (PBIAS = −6.579%). Model validation mirrored calibration in performance metrics (RMSE = 0.1233; PBIAS = −2.873%). The 95% total predictive uncertainty confidence intervals generated by GL-DREAM(ZS) enveloped close to the theoretically expected 95% of total observations for the calibration (94.5%) and validation (81.8%) periods. The performance of the GL-DREAM(ZS) approach and uncertainty assessment in this study shows promise for future MJS model applications, and the model-derived 2020 transpiration estimates highlight the MJS model utility for scaling sap-flow measurements from individual trees to stands of trees.