True Model Research Articles

Exploring the truth and beauty of theory landscapes with machine learning

Theoretical physicists describe nature by i) building a theory model and ii) determining the model parameters. The latter step involves the dual aspect of both fitting to the existing experimental data and satisfying abstract criteria like beauty, naturalness, etc. We use the Yukawa quark sector as a toy example to demonstrate how both of those tasks can be accomplished with machine learning techniques. We propose loss functions whose minimization results in true models that are also beautiful as measured by three different criteria — uniformity, sparsity, or symmetry.

2-Site versus 3-site models of ATP hydrolysis by F1-ATPase: definitive mathematical proof using combinatorics and conservation equations.

The F1-ATPase enzyme is the smallest-known molecular motor that rotates in 120° steps, driven by the hydrolysis of ATP. It is a multi-subunit enzyme that contains three catalytic sites. A central question is how the elementary chemical reactions that occur in the three sites are coupled to mechanical rotation. Various models and coupling schemes have been formulated in an attempt to answer this question. They can be classified as 2-site (bi-site) models, exemplified by Boyer's binding change mechanism first proposed 50years ago, and 3-site (tri-site) models such as Nath's torsional mechanism, first postulated 25years ago and embellished 1year back. Experimental data collated using diverse approaches have conclusively shown that steady-state ATP hydrolysis by F1-ATPase occurs in tri-site mode. Hence older models have been continually modified to make them conform to the new facts. Here, we have developed a pure mathematical approach based on combinatorics and conservation laws to test if proposed models are 2-site or 3-site. Based on this novel combinatorial approach, we have proved that older and modified models are effectively bi‒site models in that catalysis and rotation in F1-ATPase occurs in these models with only two catalytic sites occupied by bound nucleotide. Hence these models contradict consensus experimental data. The recent 2023 model of ATP hydrolysis by F1-ATPase has been proved to be a true tri-site model based on our novel mathematical approach. Such pure mathematical proofs constitute an important step forward for ATP mechanism. However, in what must be considered an aspect with great scientific potential, the power of such mathematical proofs has not been fully exploited to solve molecular biological problems, in our opinion. We believe that the creative application of pure mathematical proofs (for another example see Nath in Theory Biosci 141:249-260, 2022) can help resolve with finality various longstanding molecular-level issues that arise as a matter of course in the analysis of fundamental biological problems. Such issues have proved extraordinarily difficult to resolve by standard experimental, theoretical, or computational approaches.

A New Combination Approach for Gibbs Phenomenon Suppression in Regional Validation of Global Gravity Field Model: A Case Study in North China

A global gravity field model (GGM) is essential to be validated with ground-based or airborne observational data for the accurate application of the GGM at a regional scale. Furthermore, accurately understanding the commission errors between the GGM and observational data are crucial for improving regional gravity fields. Taking the North China region as an example, to circumvent the omission errors, it is necessary to unify the spatial resolutions of the EIGEN-6C4 model and terrestrial gravity observational data to 110 km (determined by the distribution of gravity stations) by employing the spherical harmonic function for the EIGEN-6C4 model and the Slepian basis function for the gravity data, respectively. However, the application of spherical harmonic function expansions in the gravity model results in the Gibbs phenomenon, which may be a primary factor contributing to commission errors and impedes the accurate validation of the EIGEN-6C4 model with terrestrial gravity data. To effectively mitigate this issue, this study proposes a combination approach of window function filtering and regional eigenvalue constraint (based on the Slepian basis). Utilizing the EIGEN-6C4 gravity model to derive the gravity disturbance field at a resolution of 110 km (with spherical harmonic expansion up to the 180th degree and order), the combination approach effectively suppresses over 90% of high-degree (above the 120th degree) Gibbs phenomena. This approach also reduces signal leakage outside the region, thus enhancing the spatial accuracy of the regional gravity disturbance field. A subsequent comparison of the regional gravity disturbance field derived from the true model and terrestrial gravity data in North China indicates excellent consistency, with a root mean squared error (RMSE) of 0.80 mGal. This validation confirms that the combined approach of window function filtering and regional eigenvalue constraints effectively mitigates the Gibbs phenomenon and yields precise regional gravity fields. This approach is anticipated to significantly benefit scientific applications such as improving the accuracy of regional elevation benchmarks and accurately inverting the Earth’s internal structure.

Testing times: disentangling admixture histories in recent and complex demographies using ancient DNA.

Our knowledge of human evolutionary history has been greatly advanced by paleogenomics. Since the 2020s, the study of ancient DNA has increasingly focused on reconstructing the recent past. However, the accuracy of paleogenomic methods in resolving questions of historical and archaeological importance amidst the increased demographic complexity and decreased genetic differentiation remains an open question. We evaluated the performance and behavior of two commonly used methods, qpAdm and the f3-statistic, on admixture inference under a diversity of demographic models and data conditions. We performed two complementary simulation approaches-firstly exploring a wide demographic parameter space under four simple demographic models of varying complexities and configurations using branch-length data from two chromosomes-and secondly, we analyzed a model of Eurasian history composed of 59 populations using whole-genome data modified with ancient DNA conditions such as SNP ascertainment, data missingness, and pseudohaploidization. We observe that population differentiation is the primary factor driving qpAdm performance. Notably, while complex gene flow histories influence which models are classified as plausible, they do not reduce overall performance. Under conditions reflective of the historical period, qpAdm most frequently identifies the true model as plausible among a small candidate set of closely related populations. To increase the utility for resolving fine-scaled hypotheses, we provide a heuristic for further distinguishing between candidate models that incorporates qpAdm model P-values and f3-statistics. Finally, we demonstrate a significant performance increase for qpAdm using whole-genome branch-length f2-statistics, highlighting the potential for improved demographic inference that could be achieved with future advancements in f-statistic estimations.

Rejection of parametric model errors in a recursive Kalman filter for virtual sensing applications

The performance of the Kalman filter is often hindered by the discrepancies between a model used to realize the filter and the true model of the data-generating system. While some methods to account for those errors exists, the majority is restricted to Luenberger's observers. The objective of this work is to develop a Kalman filter where the effect of parametric model errors is rejected from the state and observation equations before the development of the state estimate, and without the explicit knowledge of the error terms. For this purpose, errors in a physical parameter of a parametrized mechanical system are modelled as additive terms in the state and observation equations by means of first-order perturbation analysis. The rejection step is achieved with an injection of a shaped output nullifying the effect of the added terms. A state observer is then derived under the assumption that the injected shaped outputs and the system states are uncorrelated. The performance of the proposed approach is illustrated on an experimental data of a plate system.

Joint inversion of ERT and ambient noise surface wave data with DPC-guided fuzzy c-means clustering for near-surface imaging

SUMMARY We present a novel strategy for performing joint inversion with guided fuzzy c-means (GFCM) clustering coupling and apply it to electrical resistivity tomography (ERT) and ambient noise surface wave (ANSW) data. To accurately extract a priori clustering information, we use density peak clustering (DPC) rather than fuzzy c-means (FCM). The number and centres of resistivity and shear-wave velocity a priori clusters are extracted by DPC and then used to guide the joint inversion with the GFCM clustering coupling of ERT and ANSW data. Synthetic and field data are used to evaluate the flow and algorithm of DPC-GFCM clustering joint inversion. The results of synthetic examples show that the models recovered by the DPC-GFCM clustering joint inversion are nearly the same as the true models and are more accurate than those inverted using individual inversion and FCM-GFCM clustering joint inversion. In the field case, the depths of the stratigraphic interfaces shown in the resistivity and shear-wave velocity models inverted by DPC-GFCM clustering joint inversion are nearly consistent with those from the drilling data. In contrast, the strata recovered by the individual inversion and FCM-GFCM clustering joint inversion significantly differ from the drilling results. Both the synthetic and field examples verify the effectiveness of the DPC-GFCM clustering coupling method used for the joint inversion of ERT and ANSW data acquired from the near surface with strong heterogeneity. This novel approach can also be applied to other types of geophysical data.

A Physical TCAD Mobility Model of Amorphous In-Ga-Zn-O (a-IGZO) Devices with Spatially Varying Mobility Edges, Band-Tails, and Enhanced Low-Temperature Convergence.

Amorphous indium gallium zinc oxide (a-IGZO) is becoming an increasingly important technological material. Transport in this material is conceptualized as the heavy disorder of the material causing a conduction or mobility band-edge that randomly varies and undulates in space across the entire system. Thus, transport is envisioned as being dominated by percolation physics as carriers traverse this varying band-edge landscape of "hills" and "valleys". It is then something of a missed opportunity to model such a system using only a compact approach-despite this being the primary focus of the existing literature-as such a system can easily be faithfully reproduced as a true microscopic TCAD model with a real physically varying potential. Thus, in this work, we develop such a "microscopic" TCAD model of a-IGZO and detail a number of key aspects of its implementation. We then demonstrate that it can accurately reproduce experimental results and consider the issue of the addition of non-conducting band-tail states in a numerically efficient manner. Finally, two short studies of 3D effects are undertaken to illustrate the utility of the model: specifically, the cases of variation effects as a function of device size and as a function of surface roughness scattering.

Investigating the crystallite morphologies and aggregation in boehmite powders: a combined analysis of two- and three-dimensional electron microscopy with x-ray diffraction

Boehmite (AlOOH) is considered as an important precursor to γ-Al2O3 which when calcinated undergoes topotactic transformation to form the latter. Alumina has extensive applications in fields such as catalysis, abrasives, cosmetics among others. Boehmite falls under the category of hierarchical structures whose structural and textural properties are a result of its compositional and porous hierarchy. Although research has been carried out exquisitely to understand the complete representation of its structure, a true morphological model is an important key to understand and fully explain its transport properties during catalytic processes. 3D electron microscopy helps us to dive deeper into the different hierarchical entities of boehmite, bridging the gaps between the models and assumptions made using some more traditional characterization techniques. We present here deeper insight into the structural and morphological parameters of different commercial boehmites using 3D-TEM. Through the extraction of quantitative descriptors pertaining to hierarchical entities and subsequent comparison with bulk analyses, precise and comprehensive information regarding these microstructures can be obtained. The results of our study indicate that boehmite grades, which appear to be identical in terms of their grades, display discrepancies in the uniformity of particle sizes. Moreover, diverse platelet interactions result in varying types of pores in these grades. Furthermore, it has been observed that the interfacial interactions among various crystallographic planes exhibit variations across different specimens, thereby contributing to the distinctive compositions within the aggregates. The variation in aggregates of different boehmite grades is also reflected in the combination of four distinct quantified morphologies.

Analysis Influence: Learning True False Learning Model Based Domino Cards on Student Learning Outcomes

Purpose of the study: This study aims to determine the learning outcomes of students using and not using the True or False learning strategy based on domino cards on the reproductive system material in class XI science at Public High School 8 Bandar Lampung. Methodology: This type of research is a quasi-experimental research with a Pretest-Posttest Control Group Design research design. The sampling technique in this study was purposive sampling of 72 students. The data collection technique was carried out using 20 multiple-choice test instruments. The data analysis technique was carried out using descriptive analysis techniques and inferential analysis techniques. Main Findings: The results showed that classes that used the True or False learning strategy based on domino cards had higher average scores than classes that did not use the strategy. Regression analysis indicated a significant influence of this learning strategy on the learning outcomes of class XI science students at Public High School 8 Bandar Lampung, with a calculated t value greater than the t table and a significance level of 0.05. Novelty/Originality of this study: The novelty of this research can lie in the innovative approach in using the "True or False" learning strategy based on domino cards to understand and improve student learning outcomes in studying complex material such as Coordination Systems. This approach can be an interesting new step because it combines two different elements.

Predictive Performance of Bayesian Stacking in Multilevel Education Data

The issue of model uncertainty has been gaining interest in education and the social sciences community over the years, and the dominant methods for handling model uncertainty are based on Bayesian inference, particularly, Bayesian model averaging. However, Bayesian model averaging assumes that the true data-generating model is within the candidate model space over which averaging is taking place. Unlike Bayesian model averaging, the method of Bayesian stacking can account for model uncertainty without assuming that a true model exists. An issue with Bayesian stacking, however, is that it is an optimization technique that uses predictor-independent model weights and is, therefore, not fully Bayesian. Bayesian hierarchical stacking, proposed by Yao et al. further incorporates uncertainty by applying a hyperprior to the stacking weights. Considering the importance of multilevel models commonly applied in educational settings, this paper investigates via a simulation study and a real data example the predictive performance of original Bayesian stacking and Bayesian hierarchical stacking along with two other readily available weighting methods, pseudo-BMA and pseudo-BMA bootstrap (PBMA and PBMA+). Predictive performance is measured by the Kullback–Leibler divergence score. Although the differences in predictive performance among these four weighting methods in Bayesian stacking are small, we still find that Bayesian hierarchical stacking performs as well as conventional stacking, PBMA, and PBMA+ in settings where a true model is not assumed to exist.

A Modification to the Novel Toy Model of the Riemann Zeta Function Roots Equation

It is true that my previous series of papers about the Riemann Hypothesis has raised lots of discussions or the concerns among the mathematics society. Indeed, some professionals may think that the Riemann Zeta function at s = 1 or ∑_(n=1)^∞▒1/n is actually divergent and may tend to an infinity, how it can be acted as a denominator in a proof or something like 1/∞ ? This author’s idea is there may be no need to evaluate such kind of summation as one may consider the sum just as an (imaginary) constant (but NOT the complex number). Or we may only consider such summation as a kind of improper integrals with a black-boxed answer. In other words, the true final value of the summation ∑_(n=1)^∞▒1/n is NOT an concerning serious issue. The function ∑_(n=1)^∞▒1/n^s is in practice a smooth function and equals to log N or the logarithmic function for s = 1 or have the infinite differentiable properties except n = 0, that is why the commercial software Maple Soft can compute the Zeta function’s associated Taylor series successfully. In fact, from the above series together with the harmonic properties, one may calculate the artifical Zeta function’s root model equation such as the 0.5 +/- β*cot(ln(x))/(αx+1)n (will be discussed in my next paper of the series). Indeed, this is NOT the best model. Actually, the true Riemann Zeta non-trivial Root model equation is: {z = x + yI | z∈0.5±(y_1⩽y⩽y_2 )I} where y1= (±cot(k))/ln(k) , y2 = (±tan(k))/ln(k) with 0<k<2π. Certainly, this author cannot avoid mistakes. Some of the defects in the previous paper such as one in the linguistic proof of the RH will be stated and amended. Lastly, this author wants to remind that the present series of paper is first to establish a model equation for zeta function’s roots and the Plank’s like constant. This author then applies the telescopic and logarithmic methods for a 3 cases proof of the RH. Also, we have a Matlab code for the evaluation of RH complex contour integral. Finally, the RH problem will be transformed into a linguistic one and hence we may have solved the Riemann Hypothesis issue completely through a 4 cases of truth table collaborations with the logical inference [7] & [15] to the causality (structural) organization of the sentences [16] or just named as “Logical & Organized Context”.

Sampling weighting strategies in causal mediation analysis

BackgroundCausal mediation analysis plays a crucial role in examining causal effects and causal mechanisms. Yet, limited work has taken into consideration the use of sampling weights in causal mediation analysis. In this study, we compared different strategies of incorporating sampling weights into causal mediation analysis.MethodsWe conducted a simulation study to assess 4 different sampling weighting strategies-1) not using sampling weights, 2) incorporating sampling weights into mediation “cross-world” weights, 3) using sampling weights when estimating the outcome model, and 4) using sampling weights in both stages. We generated 8 simulated population scenarios comprising an exposure (A), an outcome (Y), a mediator (M), and six covariates (C), all of which were binary. The data were generated so that the true model of A given C and the true model of A given M and C were both logit models. We crossed these 8 population scenarios with 4 different sampling methods to obtain 32 total simulation conditions. For each simulation condition, we assessed the performance of 4 sampling weighting strategies when calculating sample-based estimates of the total, direct, and indirect effects. We also applied the four sampling weighting strategies to a case study using data from the National Survey on Drug Use and Health (NSDUH).ResultsUsing sampling weights in both stages (mediation weight estimation and outcome models) had the lowest bias under most simulation conditions examined. Using sampling weights in only one stage led to greater bias for multiple simulation conditions.DiscussionUsing sampling weights in both stages is an effective approach to reduce bias in causal mediation analyses under a variety of conditions regarding the structure of the population data and sampling methods.

Residual Stress Analysis of Re-autofrettage of a Swage-Autofrettaged Tube by Computer Modeling Incorporating Accurate Material Representation

Autofrettage processes allow engineers to reduce the thickness of thick-walled cylinders or components in high-pressure applications without sacrificing strength, life, or safety. However, during the autofrettage process, residual stresses will be generated due to plastic deformation. The complex tube material behavior is dominated by the Bauschinger effect. A better understanding and accurate prediction of the residual stress field is critical, which will enable better piping design strategies to minimize deformation and stresses under operating conditions. This study aims to predict and analyze residual stresses resulting from hydraulic re-autofrettage of a swage-autofrettaged thick-walled cylinder by computer modeling. A case study was performed on a thick-walled cylinder of A723 alloy with a radial interference of 2.5%. In order to investigate the effect of the chosen material constitutive representation, results based on the true material constitutive model were compared with the simplified prevalent material model of bi-linear kinematic strain hardening. Computer implementation for the true material was via a user-developed subroutine that incorporates the complex Bauschinger effect. The results indicate that an accurate material constitutive representation is crucial for better and more accurate prediction and understanding of residual stresses induced by autofrettage processes. Computer modeling based on the true material constitutive representation will likely prove to be a powerful tool for the design of autofrettage processes in general and thick-walled cylinders in particular.

LiteBIRD science goals and forecasts. A case study of the origin of primordial gravitational waves using large-scale CMB polarization

We study the possibility of using the LiteBIRD satellite B-mode survey to constrain models of inflation producing specific features in CMB angular power spectra. We explore a particular model example, i.e. spectator axion-SU(2) gauge field inflation. This model can source parity-violating gravitational waves from the amplification of gauge field fluctuations driven by a pseudoscalar “axionlike” field, rolling for a few e-folds during inflation. The sourced gravitational waves can exceed the vacuum contribution at reionization bump scales by about an order of magnitude and can be comparable to the vacuum contribution at recombination bump scales. We argue that a satellite mission with full sky coverage and access to the reionization bump scales is necessary to understand the origin of the primordial gravitational wave signal and distinguish among two production mechanisms: quantum vacuum fluctuations of spacetime and matter sources during inflation. We present the expected constraints on model parameters from LiteBIRD satellite simulations, which complement and expand previous studies in the literature. We find that LiteBIRD will be able to exclude with high significance standard single-field slow-roll models, such as the Starobinsky model, if the true model is the axion-SU(2) model with a feature at CMB scales. We further investigate the possibility of using the parity-violating signature of the model, such as the TB and EB angular power spectra, to disentangle it from the standard single-field slow-roll scenario. We find that most of the discriminating power of LiteBIRD will reside in BB angular power spectra rather than in TB and EB correlations.

Subseasonal Potential Predictability of Horizontal Water Vapor Transport and Precipitation Extremes in the North Pacific

Abstract Accurate forecasts of weather conditions have the potential to mitigate the social and economic damages they cause. To make informed decisions based on forecasts, it is important to determine the extent to which they could be skillful. This study focuses on subseasonal forecasts out to a lead time of four weeks. We examine the differences between the potential predictability, which is computed under the assumption of a “perfect model,” of integrated vapor transport (IVT) and precipitation under extreme conditions in subseasonal forecasts across the northeast Pacific. Our results demonstrate significant forecast skill of extreme IVT and precipitation events (exceeding the 90th percentile) into week 4 for specific areas, particularly when anomalously wet conditions are observed in the true model state. This forecast skill during weeks 3 and 4 is closely associated with a zonal extension of the North Pacific jet. These findings of the source of skillful subseasonal forecasts over the U.S. West Coast could have implications for water management in these regions susceptible to drought and flooding extremes. Additionally, they may offer valuable insights for governments and industries on the U.S. West Coast seeking to make informed decisions based on extended weather prediction. Significance Statement The purpose of this study is to understand the differences between the ability to predict high amounts of the transport of water vapor and precipitation over the North Pacific 3 and 4 weeks into the future. The results indicate that differences do exist in a region that is relevant to precipitation on the U.S. West Coast. To physically explain why differences in predictability exist, the relationship between weekly extremes of the extension of the jet stream, IVT, and precipitation over the North Pacific is explored. These findings may impact decisions relevant to water management on the U.S. West Coast susceptible to drought and flooding extremes.

Smoothness: The key factor in well-log information-assisted PINNtomo

Seismic traveltime tomography is popularly used to provide a smooth background model for subsequent depth migration or full waveform inversion (FWI). The core of the traveltime tomography process requires an eikonal solver for seismic traveltime calculation. Most of the conventional eikonal solvers, suffer from first-order convergence errors and difficulties in dealing with irregular topography, which will lead to inaccurate seismic tomography results. To solve these issues, physics-informed neural networks (PINNs) have been developed and demonstrated remarkable success in simultaneous traveltime calculation and velocity construction, which is referred to as PINNtomo. It combines the physics constraint (eikonal equation) and data constraint (traveltime fitting) to form the loss function of the networks. Though PINNtomo can manage to recover a smooth background velocity, it will fail in the inversion for a complicated high-resolution velocity model. We take advantage of the well-log velocity as a prior information to provide an additional model constraint in the loss function to improve the performance of PINNtomo. We found that the well-log information will provide high-resolution components in the inverted velocity model. However, it will destroy the lateral structure of the true model. We propose to use a smoothed version of the well-log velocity as the prior model constraint in PINNtomo. As a result, the accuracy of the inverted background velocity will be improved, which will be beneficial to the subsequent FWI.

Efficient estimation of true fixed-effects stochastic frontier models

Fixed-effects modeling has become the method of choice in several panel data settings, including models for stochastic frontier analysis. A notable instance of stochastic frontier panel data models is the true fixed-effects model, which allows disentangling unit heterogeneity from efficiency evaluations. While such a model is theoretically appealing, its estimation is hampered by incidental parameters. This note proposes a simple and rather general estimation approach where the unit-specific intercepts are integrated out of the likelihood function. We apply the theory of composite group families to the model of interest and demonstrate that the resulting integrated likelihood is a marginal likelihood with desirable inferential properties. The derivation of the result is provided in full, along with some connections with the existing literature and computational details. The method is illustrated for three notable models, given by the normal-half normal model, the heteroscedastic exponential model, and the normal-gamma model. The results of simulation experiments highlight the properties of the methodology.

Outlier Detection by Energy Minimization in Quantized Residual Preference Space for Geometric Model Fitting

Outliers significantly impact the accuracy of geometric model fitting. Previous approaches to handling outliers have involved threshold selection and scale estimation. However, many scale estimators assume that the inlier distribution follows a Gaussian model, which often does not accurately represent cases in geometric model fitting. Outliers, defined as points with large residuals to all true models, exhibit similar characteristics to high values in quantized residual preferences, thus causing outliers to cluster away from inliers in quantized residual preference space. In this paper, we leverage this consensus among outliers in quantized residual preference space by extending energy minimization to combine model error and spatial smoothness for outlier detection. The outlier detection process based on energy minimization follows an alternate sampling and labeling framework. Subsequently, an ordinary energy minimization method is employed to optimize inlier labels, thereby following the alternate sampling and labeling framework. Experimental results demonstrate that the energy minimization-based outlier detection method effectively identifies most outliers in the data. Additionally, the proposed energy minimization-based inlier segmentation accurately segments inliers into different models. Overall, the performance of the proposed method surpasses that of most state-of-the-art methods.

Simulation testing performance of ensemble models when catch data are underreported

Abstract Ensemble model use in stock assessment is increasing, yet guidance on construction and an evaluation of performance relative to single models is lacking. Ensemble models can characterize structural uncertainty and avoid the conundrum of selecting a “best” assessment model when alternative models explain observed data equally well. Through simulation, we explore the importance of identifying candidate models for both assessment and short-term forecasts and the consequences of different ensemble weighting methods on estimated quantities. Ensemble performance exceeded a single best model only when the set of candidate models spanned the true model configuration. Accuracy and precision depended on the model weighting scheme, and varied between two case studies investigating the impact of catch accuracy. Information theoretic weighting methods performed well in the case study with accurate catch, while equal weighting performed best when catch was underreported. In both cases, equal weighting produced multimodality. Ensuring that an ensemble spans the true state of nature will be challenging, but we observed that a change in sign of Mohn’s rho across candidate models coincided with the true OM being bounded. Further development of protocols to select an objective and balanced set of candidate models, and diagnostics to assess adequacy of candidates are recommended.

Accounting for technology heterogeneity in the measurement of persistent and transient inefficiency

This article tackles the problem of ignoring firm technology heterogeneity when measuring persistent and transient inefficiencies, which can lead to distorted estimates. Given that firms’ technologies exert a greater influence on long-run rather than short-run management, accounting for technology heterogeneity should prevent distortions in persistent inefficiencies and, to a lesser extent, in transient inefficiencies, which has been overlooked in the literature. Employing data from Dutch dairy farms spanning the years 2009 to 2016, I estimate random and fixed coefficients generalized true random effects models in a Bayesian framework, subsequently comparing the derived inefficiency scores. The findings indicate that average persistent inefficiency and its dispersion are inflated when technology heterogeneity is ignored, whilst transient inefficiency remains virtually unaffected. This finding is crucial to prevent firms and policy-makers from receiving misleading information about long-run performance metrics, whilst highlighting the need for future studies to account for technology heterogeneity when analyzing firms’ decisions.