Sources Of Variation Research Articles

Investigating Abiotic Sources of Spectral Variability From Multitemporal Hyperspectral Airborne Acquisitions Over the French Guyana Canopy

Investigating Abiotic Sources of Spectral Variability From Multitemporal Hyperspectral Airborne Acquisitions Over the French Guyana Canopy

Jellyfish Search Algorithm Based Feature Selection with Optimal Deep Learning for Predicting Financial Crises in the Economy and Society

The financial crises has emphasized the part of financial relationship as a potential source of macroeconomic variability and systemic risk worldwide. Predicting financial crises using deep learning (DL) infers leveraging neural network (NN) to identify patterns indicative of future financial crisis and analyse complicated financial data. DL approaches such as recurrent neural network (RNN) or long short-term memory (LSTM) that process a massive quantity of past financial data such as geopolitical events, economic indicators, and market prices. These models target to identify refined connections and signals that can lead to an economic recession by learning from earlier crisis and their precursors. The problem resides in the complex and dynamic nature of financial market, demanding continuous training and modification of methods to retain significance in the aspect of developing financial condition. Although DL shows the potential to increase prediction capabilities, it's vital to accept the inherent ambiguity in financial market and the requirement for cutting-edge development of models to enhance their accuracy and reliability. This study proposes a jellyfish search algorithm based feature selection with optimum deep learning algorithm (JSAFS-ODL) for financial crisis prediction (FCP). The objective of JSAFS-ODL technique is classified the presence of financial crises or non-financial crises. To accomplish this, the JSAFS-ODL technique applies JSA based feature selection (JSA-FS) to choose an optimum set of features. Besides, RNN-GRU model can be used for the FCP. For enhancing the detection results of the RNN-GRU approach, chimp optimization algorithm (COA) can be utilized for the optimal tuning of the hyperparameters correlated to the RNN-GRU model. To guarantee the better performance of the JSAFS-ODL procedure, a series of tests were involved. The obtained values highlighted that the JSAFS-ODL technique reaches significant performance of the JSAFS-ODL technique.

An instance of negative variance-like ‘components’ in optics by way of applying multivariable calculus to a statistical problem

In the field of optics, the design of a lens often involves analyses of the variability in an optical parameter (such as lens focal length) under manufacturing conditions, which is done in order to identify sources of high variability that may cause the optical parameter to be out of its specification. In this interdisciplinary paper, the author shares his experience in lens design, where analyses of variability are often performed through examining the partial derivatives in a multivariable Taylor series expansion of an optical parameter to second order when interactions are expected. It is shown algebraically that when the second-order expansion is applied to express a sum of squares, negative terms in the sum can occur. This instance of negative-valued “variance-like components” is interesting pedagogically in applied mathematics and statistics, as well as from an engineering standpoint, as it suggests that, based on theory, the variance of a function of random variables may not necessarily be comprised of a simple sum of positive-valued squared deviations comprising its total variance.

A computational statistical approach to assess cowpea [Vigna unguiculata (L.) Walp.] cultivars diversity and select elite genotypes from Agro-morphological and biochemical traits

Computational statistic approaches play an essential role in the evaluation and processing of agronomic, biological, and bio-medical big data. The complexity and large size of those data make computational statistics a crucial tool in bio-statistical analysis. Based on this evidence, we characterized phenotype performances of cowpea cultivar in the Northern of Côte d’Ivoire, developing our own computational statistical approach, exclusively in the R programming language. Several packages of R have been executed to assess cowpea cultivar agro-morphological and biochemical performances. Z-score clustering analysis revealed four groups of cowpeas based on agro-morphological parameters. K-mean clustering survey revealed four and two groups of cowpea cultivar respectively for agro-morphological and biochemical parameters. The Horn parallel analysis highlighted two, four, two and two agro-explanatory components and/or agro-morphological parameters as influencing data variability respectively in the first, second, third, and fourth groups of cowpea cultivars previously revealed by the k-mean analysis. The same analysis exhibited two components in terms of biochemical parameters, inducing the variability in the two cluster groups of cowpea cultivar revealed by the k-mean survey. Integrative analysis of the ANOVA test and Tukey’s multi-comparative analysis displayed yield (agro-morphological parameter) and cowpea energetic content (biochemical parameter) as main sources of cowpea cultivar phenotypical variability (P < 0.05). Of note, receiver operating characteristic predictive analysis showed the excellent performance of both cowpea yield and energetic content in selecting cowpea genetic germplasm area under the curve (AUC = 0.9). Considering as a whole, the present computational statistical approach shows excellent performance in the evaluation, characterization, and management of agro-morphological (yield parameter) and biochemical (energetic content) features of cowpea in genetic selection procedures.

Inter-scanner comparability of Z-scores for native myocardial T1 and T2 mapping

BackgroundCardiovascular Magnetic Resonance (CMR) native T1 and T2 mapping serve as robust, contrast-agent-free diagnostic tools, but hardware- and software-specific sources of variability limit the generalizability of data across CMR platforms, consequently limiting the interpretability of patient-specific parametric data. Z-scores are used to describe the relationship of observed values to the mean results as obtained in a sufficiently large normal sample. They have been successfully used to describe the severity of quantifiable abnormalities in medicine, specifically in children and adolescents. The objective of this study was to observe whether z-scores can improve the comparability of T1 and T2 mapping values across CMR scanners, field strengths, and sequences from different vendors in the same participant rather than different participants (as seen in previous studies). MethodsFifty-one healthy volunteers (26 men/25 women, mean age = 43 ± 13.51) underwent three CMR exams on three different scanners, using a Modified Look-Locker Inversion Recovery (MOLLI) 5-(3)− 3 sequence to quantify myocardial T1. For T2 mapping, a True Fast Imaging with steady-state free precession (TRUFI) sequence was used on a 3 T Skyra™ (Siemens), and a T2 Fast Spin Echo (FSE) sequence was used on 1.5 T Artist™ (GE) and 3.0 T Premier™ (GE) scanners. The averages of basal and mid-ventricular short axis slices were used to derive means and standard deviations of global mapping values. We used intra-class comparisons (ICC), repeated measures ANOVA, and paired Student’s t-tests for statistical analyses. ResultsThere was a significant improvement in intra-subject comparability of T1 (ICC of 0.11 (95% CI= −0.018, −0.332) vs 0.78 (95% CI= 0.650, 0.866)) and T2 (ICC of 0.35 (95% CI= −0.053, 0.652) vs 0.83 (95% CI= 0.726, 0.898)) when using z-scores across all three scanners. While the absolute global T1 and T2 values showed a statistically significant difference between scanners (p < 0.001), no such differences were identified using z-scores (T1z: p = 0.771; T2z: p = 0.985). Furthermore, when images were not corrected for motion, T1 z-scores showed significant inter-scanner variability (p < 0.001), resolved by motion correction. ConclusionEmploying z-scores for reporting myocardial T1 and T2 removes the variation of quantitative mapping results across different MRI systems and field strengths, improving the clinical utility of myocardial tissue characterization in patients with suspected myocardial disease.

El Niño Oscilação Sul e o acúmulo de horas de frio para a superação da dormência em fruteiras de clima temperado no sul do Brasil

ABSTRACT: The number of chilling hours is responsible for maximizing budding and flowering in temperate climate fruit trees, and this number varies widely between years. An important source of variability is the occurrence of the ENSO - El Niño Southern Oscillation. With the intent of further understanding, the objective set was to analyze the influence of ENSO on chilling hours in Rio Grande do Sul State. To perform the analysis it was used data of air temperature - below 7.2 oC - from 24 meteorological stations. The results showed that when La Niña occurs, the percentage of chilling hours was above average for the period analyzed in all locations, and when El Niño occurs, the values were below average in 79% of the locations. The highest number of chilling hours, observed in each location, mainly occurred during the presence of La Niña and the lowest number was mainly observed during the occurrence of El Niño. The largest deviation (%) of chilling hours from the average occurred in May. Despite the month of July having the highest number of chilling hours, the occurrence of El Niño or La Niña does not show any difference between them. The month of September also shows no difference when comparing instances of El Niño and La Niña.

Characterization and optimization of variability in a human colonic epithelium culture model.

Animal models have historically been poor preclinical predictors of gastrointestinal (GI) directed therapeutic efficacy and drug-induced GI toxicity. Human stem and primary cell-derived culture systems are a major focus of efforts to create biologically relevant models that enhance preclinical predictive value of intestinal efficacy and toxicity. The inherent variability in stem cell-based cultures makes development of useful models a challenge; the stochastic nature of stem cell differentiation interferes with the ability to build and validate reproducible assays that query drug responses and pharmacokinetics. In this study, we aimed to characterize and reduce sources of variability in a complex stem cell-derived intestinal epithelium model, termed RepliGut® Planar, across cells from multiple human donors, cell lots, and passage numbers. Assessment criteria included barrier for­mation and integrity, gene expression, and cytokine responses. Gene expression and culture metric analyses revealed that controlling cell passage number reduces variability and maximizes physi­ological relevance of the model. In a case study where passage number was optimized, distinct cytokine responses were observed among four human donors, indicating that biological variability can be detected in cell cultures originating from diverse human sources. These findings highlight key considerations for designing assays that can be applied to additional primary cell-derived systems, as well as establish utility of the RepliGut® Planar platform for robust development of human-pre­dictive drug-response assays.

Resting State EEG Variability and Implications for Interpreting Clinical Effect Sizes.

Resting state electroencephalography (rsEEG) is widely used to investigate intrinsic brain activity, with the potential for detecting neurophysiological abnormalities in clinical conditions from neurodegenerative disease to developmental disorders. When interpreting quantitative rsEEG changes, a key question is: how much deviation from a healthy normal brain state indicates a clinically significant change? Here, we build on the existing rsEEG variability literature by quantifying how this baseline rsEEG range can be attributed to common but underinvestigated sources of variability: experiment day, time of day, and pre-recording exercise level. We found that even within individuals, frequency band powers and entropy measures can vary by 7% (sample entropy and relative alpha power) to 28% (absolute delta power). Absolute and relative delta power increased significantly after running, while relative theta power decreased significantly. Relative beta and gamma power were significantly higher in the afternoon compared to morning trials. Sample entropy and alpha power were relatively consistent. The coefficients of variability we found are similar to some clinical rsEEG effect sizes identified in prior literature, bringing into question the clinical significance of these effect sizes. Furthermore, time of day and activity level accounted for more rsEEG variability than experiment day, indicating the potential to reduce variability by controlling for these factors in repeated-measures studies.

Sensitivity analysis and quality indicators for an in vitro oral irritation assay.

Biocompatibility testing using in vivo tests is often one of the final evaluations of new dental materials. To reduce the likelihood of failure at this late stage, predictive biocompatibility testing using in vitro methods is needed. In this study, we describe a sensitivity analysis of an oral irritation test by evalu­ating changes in the viability, using the MTT assay, of 3-D models with EpiOral constructs as a case study. Experiments that tested sources of variability in the assay led to recommendations regarding the storage of the constructs after arrival, pipetting procedure, use of MTT reagents from different vendors, use of transepithelial electrical resistance measurements, and statistical analyses. A statis­tical model was proposed to evaluate whether test substances yield a positive or negative result and the associated statistical confidence. Testing several test compounds such as the Y-4 polymer, which contains a known irritant, and dentally relevant substances such as sodium dodecyl sulfate (SDS) at varying concentrations revealed statistically significant results as expected. Lastly, a software app was designed to support a multiwell culture plate layout design. Overall, the findings and sugges­tions documented here will support the further development and potential standardization of this assay system and may be useful for the development of other assays using 3-D constructs.

AVALIAÇÃO MULTIVARIADA DA QUALIDADE DAS ÁGUAS SUPERFICIAIS E ÁGUA FINAL DE ABASTECIMENTO PÚBLICO DE BELÉM, PARÁ, BRASIL

MULTIVARIATE ASSESSMENT OF SURFACE WATER QUALITY AND FINAL PUBLIC WATER SUPPLY IN BELÉM, PARÁ, BRAZIL. The present study investigated the sources of variability in the water quality of surface springs that supply the Metropolitan Region of Belém (Pará, Brazil), as well as the treated water produced by the main water treatment station in the region. Data provided by Companhia de Saneamento do Pará were used, referring to physical-chemical and bacteriological analyzes carried out in the period from 2010 to 2020. In addition, precipitation data were considered in this study, in units of mm of rain, obtained by consulting the database of the National Institute of Meteorology. The application of principal component analysis, as an exploratory data analysis tool, allowed the grouping of the most relevant parameters of the results and proved to be efficient in reducing the data set, explaining more than 65% of the total variance, in all objects of study. Thus, it was possible to observe a general profile of the quality of surface water investigated, and, in this way, demonstrate the impact of urban pressure on the environment, as well as the need for maintenance and conservation of surface water resources.

A review of toxicity assessment procedures of solar photovoltaic modules

Environmental management of solar photovoltaic (PV) modules is attracting attention as a growing number of field-operated PV modules approach end of life (EoL). PV modules may contain small amounts of toxic metals, and the procedures for assessing and regulating the toxic metal content and release of such materials at EoL differ widely across nations. This paper provides an overview of the metal composition of PV modules and common procedures for toxicity assessment through extensive research and review of technical literature and legislative documents. This review focuses on three primary aspects: first, it explores the distribution of toxic elements within current and emerging PV module designs, with a specific focus on obtaining representative samples for proportional toxicity testing within different module laminate areas. Second, it examines a sampling standard and the diverse toxicity testing methods and regulations employed in various regions, encompassing standards like the Environmental Protection Agency (EPA) Test Method 1311 (Toxicity Characteristic Leaching Procedure, TCLP) in the U.S., Restriction of Hazardous Substances (RoHS) in Europe, and the Waste Extraction Test (WET) in California. Third, the review examines the sources of variability in toxicity testing outcomes, including techniques for securing homogeneous samples from non-uniform PV modules, selecting particle sizes representative of landfill conditions in extracted samples, determining appropriate leachate characteristics such as leaching agents and pH levels, and considering factors like test duration and temperatures. In summary, this review summarizes relevant regulations and offers a comprehensive overview of the strengths and limitations associated with several toxicity assessment procedures currently in practice.

Jitter due to Random Telegraph Noise: a Study on the Time Dependent Variability of a Ring Oscillator

Random Telegraph Noise is a relevant source of variability in integrated circuits and a growing issue due to device scaling. Jitter is one of its consequences; therefore, it is an important parameter of performance measurements for electronic components and systems. In this paper, the relationship between Random Telegraph Noise and different concepts of jitter is studied. Firstly, a gate delay variability study of a CMOS inverter is discussed. Then, an absolute, a periodic, and a cycle-to-cycle jitter are evaluated in a five-stage ring oscillator. All the data were generated with a spice simulator modified to properly account for Random Telegraph Noise, using the Monte Carlo technique.

Remaining Useful Life Prediction for Two-Phase Nonlinear Degrading Systems with Three-Source Variability.

Recently, the estimation of remaining useful life (RUL) for two-phase nonlinear degrading devices has shown rising momentum for ensuring their safe and reliable operation. The degradation processes of such systems are influenced by the temporal variability, unit-to-unit variability, and measurement variability jointly. However, current studies only consider these three sources of variability partially. To this end, this paper presents a two-phase nonlinear degradation model with three-source variability based on the nonlinear Wiener process. Then, the approximate analytical solution of the RUL with three-source variability is derived under the concept of the first passage time (FPT). For better implementation, the offline model parameter estimation is conducted by the maximum likelihood estimation (MLE), and the Bayesian rule in conjunction with the Kalman filtering (KF) algorithm are utilized for the online model updating. Finally, the effectiveness of the proposed approach is validated through a numerical example and a practical case study of the capacitor degradation data. The results show that it is necessary to incorporate three-source variability simultaneously into the RUL prediction of the two-phase nonlinear degrading systems.

Collisions of RNA polymerases behind the replication fork promote alternative RNA splicing in newly replicated chromatin

DNA replication produces a global disorganization of chromatin structure that takes hours to be restored. However, how these chromatin rearrangements affect the regulation of gene expression and the maintenance of cell identity is not clear. Here, we use ChOR-seq and ChrRNA-seq experiments to analyze RNA polymerase II (RNAPII) activity and nascent RNA synthesis during the first hours after chromatin replication in human cells. We observe that transcription elongation is rapidly reactivated in nascent chromatin but that RNAPII abundance and distribution are altered, producing heterogeneous changes in RNA synthesis. Moreover, this first wave of transcription results in RNAPII blockages behind the replication fork, leading to changes in alternative splicing. Altogether, our results deepen our understanding of how transcriptional programs are regulated during cell division and uncover molecular mechanisms that explain why chromatin replication is an important source of gene expression variability.

(Invited) Simulations of Ultra-Scaled Electronic Devices with a Novel Flexible Nano-TCAD Nano-Electronic Simulation Software (NESS) Environment

Simulation of conventional and emerging electronic devices using Technology Computer Aided Design (TCAD) tools has been an essential part of the semiconductor industry as well as academic research. Computational efficiency and accuracy of the numerical modeling are the key criteria on which quality and utility of a TCAD tool is ascertained. Further, the ability of the tool to incorporate different modeling paradigms and to be applicable to a wide range of device architectures and operating conditions is essential.In this talk, I will provide an overview of the new device simulator NESS (Nano-Electronic Software Simulator) developed at Device Modelling Group at University of Glasgow. NESS is a fast, modular software with its own structure and mesh generators, and contains different modules for classical, semi-classical, and quantum transport solvers, mobility calculation, kinetic Monte-Carlo etc. NESS can simulate numerous device architectures such as nanowire and bulk transistors, tunnelling field effect transistors (TFET) and resonant tunnelling diodes. Moreover, NESS accounts for various sources of statistical variability in nano-size electronic devices and it can perform statistical simulations for thousands microscopically different devices.

Test-retest reliability and short-term variability of quantitative light reflex pupillometry in a mixed memory clinic cohort

BackgroundQuantitative light reflex pupillometry (qLRP) may be a promising digital biomarker in neurodegenerative diseases such as Alzheimer's disease (AD), as neuropathological changes have been found in the midbrain structures governing the light reflex. Studies investigating test-retest reliability and short-term, intra-subject variability of qLRP in these patient groups are missing. Our objective was therefore to investigate the test-retest reliability and short-term, intra-subject variability of qLRP in a memory clinic setting, where patients with neurodegenerative disease are frequently evaluated. MethodsTest-retest reliability study. We recruited patients from a tertiary memory clinic and qLRP was carried out at a baseline visit and then repeated on day 3–14 and on day 21–35 using a hand-held pupillometer. We evaluated the test-retest reliability of qLRP by calculating intraclass correlation coefficients (ICCs) and intra-subject, short-term variability by fitting linear mixed models. We compared ICCs for subgroups based on age, sex, disease severity (MCI vs. mild dementia), AD diagnosis, and amount of neurodegeneration (cerebrospinal fluid-total tau levels). ResultsIn total, 40 patients (mean age 72 years, 15 female, 22 with mild dementia) were included in the study. We found good-excellent reliability (ICC range 0.86–0.93) for most qLRP parameters. qLRP parameters exhibited limited intra-subject variability and we found no large sources of variability when examining subgroups. ConclusionqLRP was found to have acceptable test-retest reliability and the study results pave the way for research using longitudinal or cross-sectional measurements to assess the construct in identifying and prognosticating neurodegenerative diseases.

Optimal planning target margin for prostate radiotherapy based on interfractional and intrafractional variability assessment during 1.5T MRI-guided radiotherapy.

We analyzed daily pre-treatment- (PRE) and real-time motion monitoring- (MM) MRI scans of patients receiving definitive prostate radiotherapy (RT) with 1.5 T MRI guidance to assess interfractional and intrafractional variability of the prostate and suggest optimal planning target volume (PTV) margin. Rigid registration between PRE-MRI and planning CT images based on the pelvic bone and prostate anatomy were performed. Interfractional setup margin (SM) and interobserver variability (IO) were assessed by comparing the centroid values of prostate contours delineated on PRE-MRIs. MM-MRIs were used for internal margin (IM) assessment, and PTV margin was calculated using the van Herk formula. We delineated 400 prostate contours on PRE-MRI images. SM was 0.57 ± 0.42, 2.45 ± 1.98, and 2.28 ± 2.08 mm in the left-right (LR), anterior-posterior (AP), and superior-inferior (SI) directions, respectively, after bone localization and 0.76 ± 0.57, 1.89 ± 1.60, and 2.02 ± 1.79 mm in the LR, AP, and SI directions, respectively, after prostate localization. IO was 1.06 ± 0.58, 2.32 ± 1.08, and 3.30 ± 1.85 mm in the LR, AP, and SI directions, respectively, after bone localization and 1.11 ± 0.55, 2.13 ± 1.07, and 3.53 ± 1.65 mm in the LR, AP, and SI directions, respectively, after prostate localization. Average IM was 2.12 ± 0.86, 2.24 ± 1.07, and 2.84 ± 0.88 mm in the LR, AP, and SI directions, respectively. Calculated PTV margin was 2.21, 5.16, and 5.40 mm in the LR, AP, and SI directions, respectively. Movements in the SI direction were the largest source of variability in definitive prostate RT, and interobserver variability was a non-negligible source of margin. The optimal PTV margin should also consider the internal margin.

The impact of ENSO and NAO initial conditions and anomalies on the modeled response to Pinatubo-sized volcanic forcing

Abstract. Strong, strato-volcanic eruptions are a substantial, intermittent source of natural climate variability. Initial atmospheric and oceanic conditions, such as El Niño Southern Oscillation (ENSO) and the North Atlantic Oscillation (NAO), also naturally impact climate on interannual timescales. We examine how initial conditions of ENSO and NAO contribute to the evolution of climate in the period following a Pinatubo-type eruption using a large (81-member) ensemble of model simulations in GISS model E2.1-G. Simulations are initialized from sampled conditions of ENSO and NAO using the protocol of the coordinated CMIP6 Volcanic Model Intercomparison Project (VolMIP) – where aerosols are forced with respect to time, latitude, and height. We analyze paired anomalous variations (perturbed – control) to understand changes in global and regional climate responses under positive, negative, and neutral ENSO and NAO conditions. In particular, we find that for paired anomalies there is a high probability of strong (∼1.5 ∘C) warming of northern Eurasia surface air temperature in the first winter after the volcanic eruption for negative NAO ensembles coincident with decreased lower stratospheric temperature at the poles, decreased geopotential height, and strengthening of the stratospheric polar vortex. Climate anomalies (relative to average conditions across the control period), however, show no mean warming and suggest that the strength of this response is impacted by conditions present in the selected period of the control run. Again using paired anomalies, we also observe that under both +ENSO and −ENSO ensembles sea surface temperature decreases in the first post-eruptive boreal winter coinciding with surface cooling from volcanic aerosols. Neutral ENSO ensembles, on the other hand, show variability in their response with no clear trend in post-eruptive warming or cooling. In general, paired anomalies from unperturbed simulations give insight into the evolution of the climate response to volcanic forcing; however, when compared with anomalies from climatological conditions, it is clear that paired anomalies are significantly affected by sampled initial conditions occurring at the time of the volcanic eruption.

Effectiveness of DNA barcoding, SCOT markers and phytochemical characterization in biodiversity assessment of some Zea mays hybrids

Because of the rapid fluctuation in world's climate, politics, and economy, maize outputs must be increased to meet food security demands. Due to the importance of the genetic materials under study as a source of quality traits and genetic variability, this study was designed to examine the effectiveness and strength of assessing biodiversity in some maize hybrids using barcodes and molecular SCoTs markers in addition to phytochemical characterization. Using matK and rbcL sequences, pairwise distances were obtained and assessed. The hybrids under study were distributed into different groups based on the matK and rbcL sequences. The 12 SCoT primers amplified 155 amplicons with an average of 12.92 bands per primer, of which 63 monomorphic bands (MB) (40.65 %) and 92 polymorphic band (PB) (59.35 %). Additionally, the number of unique bands was six bands. The SCoT-6 primer produced three bands individually, while the primers SCoT-2, SCoT-3, SCoT-4, and SCoT-7 produced one band each. Hybrids S.C. 10, S.C. 128, S.C. 130, S.C. 131, S.C. 132 and TWC. 324 were more influenced by some primary constituents and bioactive secondary compounds such as moisture content (%), crude fiber, total carbohydrates, total sugars, soluble carbohydrates, non-reducing sugars, phenolics compounds, and tannins. The hybrids S.C. 168, S.C. 176, S.C. 178, TWC. 360, TWC. 368 and TWC. 321 were more influenced by the SCoT molecular markers. The results of genetic variation between the studied hybrids could be considered a potentially source of genetic information in developing future breeding programs.

Quantifying modeling uncertainties in seismic analysis of dams: Insights from an international benchmark study

AbstractAdvances in nonlinear dynamic analysis of dams have not completely resolved concerns over modeling confidence and analysis accuracy. Verification and validation offer accuracy assessment, but uncertainties persist during performance evaluation due to both epistemic (modeling) and aleatory (parametric) sources. Epistemic uncertainties arise from simplifications and modeling techniques. This paper addresses epistemic uncertainties in a gravity dam seismic analysis using data from the International Comnission on Large Dams (ICOLD) benchmark study. While the benchmark formulation included the finite element model of the dam, mechanical material properties, and dynamic loads, participants retained the flexibility to opt for best‐practice modeling assumptions, simplifications, and other specifics. Notable response variability emerged, particularly in crack profiles and damage predictions. This study examines sources of variability, quantifying modeling uncertainty for the benchmark problem. More specifically, the modeling variability is quantified using the logarithmic standard deviation, also known as dispersion. This metric enables its incorporation into other seismic risk assessment and fragility studies. Under relatively low‐intensity motion (peak ground acceleration [PGA] of 0.18 g in this case), modeling dispersion of 0.45, 0.30, 0.32, and 0.30 were calculated for the maximum dynamic crest displacement, maximum hydrodynamic pressure at the heel, heel and crest maximum acceleration, respectively. Additionally, the dispersion of the failure PGA was determined to be 0.7. Findings underscore the need for systematic seismic response modeling in dam engineering to enhance prediction accuracy. A better understanding of the sources and magnitudes of modeling uncertainties can help improve the reliability of dam seismic analysis and contribute to the development of more effective risk assessment and mitigation strategies.