Quantitative Comparison Of Models Research Articles

An optimal Bayesian strategy for comparing Wiener–Hunt deconvolution models in the absence of ground truth

Abstract This paper considers the quantitative comparison of several alternative models to perform deconvolution in situations where there is no ground truth data available. With applications to very large data sets in mind, we focus on linear deconvolution models based on a Wiener filter. Although comparatively simple, such models are widely prevalent in large scale setting such as high-resolution image restoration because they provide an excellent trade-off between accuracy and computational effort. However, in order to deliver accurate solutions, the models need to be properly calibrated in order to capture the covariance structure of the unknown quantity of interest and of the measurement error. This calibration often requires onerous controlled experiments and extensive expert supervision, as well as regular recalibration procedures. This paper adopts an unsupervised Bayesian statistical approach to model assessment that allows comparing alternative models by using only the observed data, without the need for ground truth data or controlled experiments. Accordingly, the models are quantitatively compared based on their posterior probabilities given the data, which are derived from the marginal likelihoods or evidences of the models. The computation of these evidences is highly non-trivial and this paper consider three different strategies to address this difficulty—a Chib approach, Laplace approximations, and a truncated harmonic expectation—all of which efficiently implemented by using a Gibbs sampling algorithm specialised for this class of models. In addition to enabling unsupervised model selection, the output of the Gibbs sampler can also be used to automatically estimate unknown model parameters such as the variance of the measurement error and the power of the unknown quantity of interest. The proposed strategies are demonstrated on a range of image deconvolution problems, where they are used to compare different modelling choices for the instrument’s point spread function and covariance matrices for the unknown image and for the measurement error.

Seismic Tomography 2024

ABSTRACT Seismic tomography is the most abundant source of information about the internal structure of the Earth at scales ranging from a few meters to thousands of kilometers. It constrains the properties of active volcanoes, earthquake fault zones, deep reservoirs and storage sites, glaciers and ice sheets, or the entire globe. It contributes to outstanding societal problems related to natural hazards, resource exploration, underground storage, and many more. The recent advances in seismic tomography are being translated to nondestructive testing, medical ultrasound, and helioseismology. Nearly 50 yr after its first successful applications, this article offers a snapshot of modern seismic tomography. Focused on major challenges and particularly promising research directions, it is intended to guide both Earth science professionals and early-career scientists. The individual contributions by the coauthors provide diverse perspectives on topics that may at first seem disconnected but are closely tied together by a few coherent threads: multiparameter inversion for properties related to dynamic processes, data quality, and geographic coverage, uncertainty quantification that is useful for geologic interpretation, new formulations of tomographic inverse problems that address concrete geologic questions more directly, and the presentation and quantitative comparison of tomographic models. It remains to be seen which of these problems will be considered solved, solved to some extent, or practically unsolvable over the next decade.

A Quantitative Comparison of Mortality Models with Jumps: Pre- and Post-COVID Insights on Insurance Pricing

Population events such as natural disasters, pandemics, extreme weather, and wars might cause jumps that have an immediate impact on mortality rates. The recent COVID-19 pandemic has demonstrated that these events should not be treated as nonrepetitive exogenous interventions. Therefore, mortality models incorporating jump effects are particularly important to capture the adverse mortality shocks. The mortality models with jumps, which we consider in this study, differ in terms of the duration of the jumps–transitory or permanent–the frequency of the jumps, and the size of the jumps. To illustrate the effect of the jumps, we also consider benchmark mortality models without jump effects, such as the Lee-Carter model, Renshaw and Haberman model and Cairns-Blake-Dowd model. We discuss the performance of all the models by analysing their ability to capture the mortality deterioration caused by COVID-19. We use data from different countries to simulate the mortality rates for the pandemic and post-pandemic years and examine their accuracy in forecasting the mortality jumps due to the pandemic. Moreover, we also examine the jump-free and jump models in terms of their impact on insurance pricing, specifically term annuity and life insurance present values calibrated for both pre- and post-COVID data.

Quantitative comparison of ocean soundscape model and measurements in the Atlantic outer continental shelf

For 4 years, the Atlantic Deep Water Ecosystem Observing Network (ADEON), measured the acoustic soundscape using 7 bottom mounted landers off the coast of the South East United States (from Florida to Virginia). A shipping and noise model was built to generate regional sound maps, as well as to perform site-specific modeling for frequencies ranging from 20 Hz to 3150 Hz. The regional sound maps reveal that water depth is the key driver to local sound levels followed by the local geo-acoustic properties. It is predicted that the sound can be as much as 10 dB higher on the seafloor than at depths of 10 m. The site specific (where landers were deployed) model was run with a wide range of sediment uncertainty in the model, given our lack of knowledge of the sediment structure in water depths of 200 to 800 m deep, far from shore. A simple qualitative comparison of the soundscape measurements with the model provides immediate insight into the type of sediment at each lander position. Using this estimated sediment type, quantitative comparisons between the model and the data are made.

Quantitative comparison and performance evaluation of deep learning-based object detection models on edge computing devices

Low latency in the detection of objects in images is a fundamental aspect to provide an immediate response in different application scenarios, and to carry out the relevant actions. Deep learning-based models offer better results than traditional object detection techniques. As a result of this increased accuracy, the computational cost of these models is often very high. For this reason, several edge computing devices have emerged that perform inference quickly and efficiently due to the incorporation of hardware accelerators. In this work, different devices are evaluated using object detection algorithms based on deep learning. For this purpose, YOLOv3, YOLOv5 and YOLOX, with all their variants, have been used on an NVIDIA Jetson Nano, an NVIDIA Jetson AGX Xavier and a Google Coral Dev Board. For the evaluation of the models and devices, one of the most widespread datasets, MS COCO, is used. In addition, they have been evaluated using twenty different input sizes and three frameworks (Pytorch, TensorRT and Tensorflow Lite). From the data obtained, data can be extrapolated to other models such as YOLOv8. Additionally, the FPS/Power Consumption and FPS/Cost ratios are analyzed, as well as their feasibility in a real use scenario. As a result of this work, valuable recommendations are provided for projects where this technology is to be applied.

Further perceptions of probability: In defence of associative models.

Extensive research in the behavioral sciences has addressed people's ability to learn stationary probabilities, which stay constant over time, but only recently have there been attempts to model the cognitive processes whereby people learn-and track-nonstationary probabilities. In this context, the old debate on whether learning occurs by the gradual formation of associations or by occasional shifts between hypotheses representing beliefs about distal states of the world has resurfaced. Gallistel et al. (2014) pitched the two theories against each other in a nonstationary probability learning task. They concluded that various qualitative patterns in their data were incompatible with trial-by-trial associative learning and could only be explained by a hypothesis-testing model. Here, we contest that claim and demonstrate that it was premature. First, we argue that their experimental paradigm consisted of two distinct tasks: probability tracking (an estimation task) and change detection (a decision-making task). Next, we present a model that uses the (associative) delta learning rule for the probability tracking task and bounded evidence accumulation for the change detection task. We find that this combination of two highly established theories accounts well for all qualitative phenomena and outperforms the alternative model proposed by Gallistel et al. (2014) in a quantitative model comparison. In the spirit of cumulative science, we conclude that current experimental data on human learning of nonstationary probabilities can be explained as a combination of associative learning and bounded evidence accumulation and does not require a new model. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Policy Design for Electricity Efficiency: A Case Study of Bottom-Up Energy Modeling in the Residential Sector and Buildings

Energy models play a crucial role in the domain of energy policy by serving as essential instruments for decision-making. However, a significant limitation of numerous bottom-up energy models (BUEMs) is their empirical design, which hinders their ability to effectively inform policy design. This study presents a structured framework that can be used to improve the effectiveness of behavior, understanding, and engagement measures in the development of BUEMs for enhancing energy end-use efficiency. The model selected for this case study was provided by the Mexican Commission for the Efficient Use of Energy (CONUEE), and it examines the impact of regulatory instruments on the residential sector and residential buildings. The benefits of the proposed framework were successfully demonstrated through a quantitative comparison of real energy models, using and without using the said framework, revealing the advantages of its use. The framework significantly decreases the time required for model generation in various aspects by 59.43%. The obtained results highlight the effectiveness of the framework, and it could enhance the existing knowledge in the sector.

Комплексная оценка аналитических и численных моделей эфемерид планет Солнечной системы на примере околоземных КА

The paper presents a developed algorithm for using the high-precision numerical ephemerid models. Qualitative and quantitative comparison of analytical and numerical high-precision planetary ephemerid models was carried out, including NASA averaged elements model, Newcomb model, Mees model, DE/LE series models and EPM series models. Absolute values of the radius vector residuals for each planet of the Solar System were obtained within the framework of the considered ephemerid models. Integration methods were evaluated, including Runge—Kutta method of the 4th order, Adams method of the 16th order and Dorman-Prince method of the 5(4)th order. Results of evaluating accuracy and efficiency of calculations depending on the integration stage were obtained. Values of perturbing accelerations from the Sun, Moon and planets for a GLONASS-type spacecraft were calculated. Practical recommendations are provided for using analytical and numerical planetary ephemerides in simulation of the near-Earth spacecraft motion.

Comparison and Analysis of Several Quantitative Identification Models of Pesticide Residues Based on Quick Detection Paperboard

Pesticide residues have long been a significant aspect of food safety, which has always been a major social concern. This study presents research and analysis on the identification of pesticide residue fast detection cards based on the enzyme inhibition approach. In this study, image recognition technology is used to extract the color information RGB eigenvalues from the detection results of the quick detection card, and four regression models are established to quantitatively predict the pesticide residue concentration indicated by the quick detection card using RGB eigenvalues. The four regression models are linear regression model, quadratic polynomial regression model, exponential regression model and RBF neural network model. Through study and comparison, it has been shown that the exponential regression model is superior at predicting the pesticide residue concentration indicated by the rapid detection card. The correlation value is 0.900, and the root mean square error is 0.106. There will be no negative prediction value when the expected concentration is near to 0. This gives a novel concept and data support for the development of image recognition equipment for pesticide residue fast detection cards based on the enzyme inhibition approach.

Human-Like Modulation Sensitivity Emerging through Optimization to Natural Sound Recognition.

Natural sounds contain rich patterns of amplitude modulation (AM), which is one of the essential sound dimensions for auditory perception. The sensitivity of human hearing to AM measured by psychophysics takes diverse forms depending on the experimental conditions. Here, we address with a single framework the questions of why such patterns of AM sensitivity have emerged in the human auditory system and how they are realized by our neural mechanisms. Assuming that optimization for natural sound recognition has taken place during human evolution and development, we examined its effect on the formation of AM sensitivity by optimizing a computational model, specifically, a multilayer neural network, for natural sound (namely, everyday sounds and speech sounds) recognition and simulating psychophysical experiments in which the AM sensitivity of the model was assessed. Relatively higher layers in the model optimized to sounds with natural AM statistics exhibited AM sensitivity similar to that of humans, although the model was not designed to reproduce human-like AM sensitivity. Moreover, simulated neurophysiological experiments on the model revealed a correspondence between the model layers and the auditory brain regions. The layers in which human-like psychophysical AM sensitivity emerged exhibited substantial neurophysiological similarity with the auditory midbrain and higher regions. These results suggest that human behavioral AM sensitivity has emerged as a result of optimization for natural sound recognition in the course of our evolution and/or development and that it is based on a stimulus representation encoded in the neural firing rates in the auditory midbrain and higher regions.SIGNIFICANCE STATEMENT This study provides a computational paradigm to bridge the gap between the behavioral properties of human sensory systems as measured in psychophysics and neural representations as measured in nonhuman neurophysiology. This was accomplished by combining the knowledge and techniques in psychophysics, neurophysiology, and machine learning. As a specific target modality, we focused on the auditory sensitivity to sound AM. We built an artificial neural network model that performs natural sound recognition and simulated psychophysical and neurophysiological experiments in the model. Quantitative comparison of a machine learning model with human and nonhuman data made it possible to integrate the knowledge of behavioral AM sensitivity and neural AM tunings from the perspective of optimization to natural sound recognition.

On the Construction of Mathematical Models of the Membrane Theory of Convex Shells

Introduction. The paper considers the issues of constructing mathematical models of the momentless equilibrium stress state of elastic convex shells using methods of the complex analysis. At the same time, shells with a piecewise smooth (ribbed) lateral surface were considered for the first time. The work objective was to find classes of shells for which it is possible to build meaningful mathematical models.Materials and Methods. Using the methods of the theory of the discontinuous Riemann-Hilbert problem for generalized analytic functions, a criterion for the unconditional solvability of the corresponding static problem for the equilibrium equation of a convex shell with a ribbed lateral surface has been obtained. This criterion, combined with the methods of the theory of generalized analytical functions, is a tool for constructing mathematical models of the state of momentless stress equilibrium of elastic convex shells.Results. A method has been developed for constructing mathematical models of the momentless equilibrium stress state of a convex shell under the action of a variable external load and the condition of stress concentration at the corner points of the median surface. The introduction of a vector parameter, as well as the concepts of “order of quasi-correctness” and “quasi-stability”, into the boundary condition provided both quantitative and qualitative comparison of mathematical models. Classes of shells have been found for which the description of mathematical models is given in terms of the geometry of the boundary in the vicinity of the corner points of the median surface. The obtained result, when applied to shallow convex shells, provides a geometric criterion of quasi-stability. It is established that for a shallow shell, which is not quasi-stable, the only adequate mathematical model is a probabilistic one.Discussion and Conclusions. The proposed method for constructing a two-parameter family of problems with a modified boundary condition makes it possible to simulate the momentless equilibrium stress state for fairly wide classes of convex shells with a piecewise-smooth lateral surface under a sleeve connection. At the same time, the developed algorithm for calculating the boundary condition index allowed us to answer the question of the existence of an adequate mathematical model for a shell with a side surface of an arbitrary configuration, and for shells of a special type (specifically, shallow or shells of revolution), to formulate a geometric criterion for the existence of a mathematical model.

Number feature distortion modulates cue-based retrieval in reading

In sentence comprehension, what are the cognitive constraints that determine number agreement computation? Two broad classes of theoretical proposals are: (i) Representation distortion accounts, which assume that the number feature on the subject noun gets overwritten probabilistically by the number feature on a non-subject noun, leading to a non-veridical memory trace of the subject noun; and (ii) The cue-based retrieval account, a general account of dependency completion processes which assumes that the features on the subject noun remain intact, and that processing difficulty is only a function of the memory constraints on dependency completion. However, both these classes of model fail to account for the full spectrum of number agreement patterns observed in published studies. Using 17 benchmark datasets on number agreement from four languages, we implement seven computational models: three variants of representation distortion, two cue-based retrieval models, and two hybrid models that assume both representation-distortion and retrieval. Quantitative model comparison shows that the best fit is achieved by a hybrid model that assumes both feature distortion (specifically, feature percolation) and cue-based retrieval; numerically, the second-best quantitative fit was achieved by a distortion-based model of number attraction that assumes grammaticality bias during reading. More broadly, the work furnishes comprehensive evidence to support the idea that cue-based retrieval theory, which aims to be a general account of dependency completion, needs to incorporate a feature distortion process.

A Quantitative Comparison of High Latitude Electric Field Models During a Large Geomagnetic Storm

AbstractModels of the high‐latitude ionospheric electric field (EF) are commonly used to specify the magnetospheric forcing in thermosphere or whole atmosphere models. The use of decades‐old models based on spacecraft data is still widespread. Currently the Heelis et al. (1982, https://doi.org/10.1029/ja087ia08p06339) and Weimer (2005b, https://doi.org/10.1029/2005ja011270) climatology models are most commonly used but it is possible a more recent EF model could improve forecasting functionality. Modern EF models, derived from radar data, have been developed to incorporate advances in data availability (Bristow et al., 2022, https://doi.org/10.1029/2021sw002920; Thomas & Shepherd, 2018, https://doi.org/10.1002/2018ja025280; Walach et al., 2022, https://doi.org/10.1029/2021ja029559). It is expected that climatologies based on this larger and up‐to‐date data set will better represent the high latitude ionosphere and improve forecasting abilities. An example of two such models, which have been developed using line‐of‐sight velocity measurements from the Super Dual Auroral Radar Network (SuperDARN) are the Thomas and Shepherd model (TS18) (Thomas & Shepherd, 2018, https://doi.org/10.1002/2018ja025280), and Walach and Grocott geomagnetic Storm model (WGS21) (Walach et al., 2021, https://doi.org/10.1029/2020ja028512). Here we compare the outputs of these EF models during the September 2017 storm, covering a range of solar wind and interplanetary magnetic field (IMF) conditions. We explore the relationships between the IMF conditions and the model output parameters such as transpolar voltage, the polar cap size and the lower latitude boundary of convection. We find that the electric potential and field parameters from the spacecraft‐based models have a significantly higher magnitude than the SuperDARN‐based models. We discuss the similarities and differences in topology and magnitude for each model.

Contrasting exemplar and prototype models in a natural-science category domain.

A classic issue in the cognitive psychology of human category learning has involved the contrast between exemplar and prototype models. However, experimental tests to distinguish the models have relied almost solely on use of artificially-constructed categories composed of simplified stimuli. Here we contrast the predictions from the models in a real-world natural-science category domain-geologic rock types. Previous work in this domain used a set of complementary methods, including multidimensional scaling and direct dimension ratings, to derive a high-dimensional feature space in which the rock stimuli are embedded. The present work compares the category-learning predictions of exemplar and prototype models that make reference to this derived feature space. The experiments include conditions that should be favorable to prototype abstraction, including use of multiple large-size categories, delayed transfer testing, and real-world category structures. Nevertheless, the results of the qualitative and quantitative model comparisons point toward the exemplar model as providing a far better account of the observed results. Evidence is also provided that participants do not rely on all-or-none rote memories for the stored exemplars but rather use remembered exemplars as a basis for generalizing to novel transfer items from the learned categories. Limitations and directions of future work are discussed. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Multi‐Stage Phase‐Segregation of Mixed Halide Perovskites under Illumination: A Quantitative Comparison of Experimental Observations and Thermodynamic Models

AbstractPhoto‐ and charge‐carrier‐induced ion migration is a major challenge when utilizing metal halide perovskite semiconductors for optoelectronic applications. For mixed iodide/bromide perovskites, the compositional instability due to light‐ or electrical bias induced phase‐segregation restricts the exploitation of the entire bandgap range. Previous experimental and theoretical work suggests that excited states or charge carriers trigger the process, but the exact mechanism is still under debate. To identify the mechanism and cause of light‐induced phase‐segregation phenomena, the full compositional range of methylammonium lead bromide/iodide samples are investigated, MAPb(BrxI1‐x)3 with x = 0…1, by simultaneous in situ X‐ray diffraction (XRD) and photoluminescence (PL) spectroscopy during illumination. The quantitative comparison of composition‐dependent in situ XRD and PL shows that at excitation densities of 1 sun, only the initial stage of photo‐segregation is rationalized with the previously established thermodynamic models. However, a progression of the phase segregation is observed that is rationalized by considering long‐lived accumulative photo‐induced material alterations. It is suggested that (additional) photo‐induced defects, possibly halide vacancies and interstitials, need to be considered to fully rationalize light‐induced phase segregation and anticipate the findings to provide crucial insight for the development of more sophisticated models.

Neuro-cognitive models of single-trial EEG measures describe latent effects of spatial attention during perceptual decision making

Visual perceptual decision-making involves multiple components including visual encoding, attention, accumulation of evidence, and motor execution. Recent research suggests that EEG signals can identify the time of encoding and the onset of evidence accumulation during perceptual decision-making. Although scientists show that spatial attention improves participant performance in decision making, little is known about how spatial attention influences the individual cognitive components that give rise to that improvement in performance. We found evidence in this work that both visual encoding time (VET) before evidence accumulation and other non-decision time processes after or during evidence accumulation are influenced by spatial top-down attention. Specifically, we used an open-source dataset in which participants were informed about the location of a target stimulus in the visual field on some trials during a face-car perceptual decision-making task. Fitting neural drift–diffusion models to response time, accuracy, and single-trial N200 latencies (∼ 125 to 225 ms post-stimulus) of EEG allowed us to separate the processes of visual encoding and the decision process from other non-decision time processes such as motor execution. These models were fitted in a single step in a hierarchical Bayesian framework. Quantitative model comparison to simulation-based theories reveals that spatial attention manipulates both VET and other non-decision time processes. We discuss why spatial attention may affect other non-evidence accumulation processes, such as motor execution time (MET), and why this may seem unexpected given the literature. We provide recommendations for future work to deal with this topic by a combination of neuro-cognitive models and model simulations at the single-trial level.

On the assumptions behind metacognitive measurements: Implications for theory and practice.

Theories of visual confidence have largely been grounded in the gaussian signal detection framework. This framework is so dominant that idiosyncratic consequences from this distributional assumption have remained unappreciated. This article reports systematic comparisons of the gaussian signal detection framework to its logistic counterpart in the measurement of metacognitive accuracy. Because of the difference in their distribution kurtosis, these frameworks are found to provide different perspectives regarding the efficiency of confidence rating relative to objective decision (the logistic model intrinsically gives greater meta-dʹ/dʹ ratio than the gaussian model). These frameworks can also provide opposing conclusions regarding the metacognitive inefficiency along the internal evidence continuum (whether meta-dʹ is larger or smaller for higher levels of confidence). Previous theories developed on these lines of analysis may need to be revisited as the gaussian and logistic metacognitive models received somewhat equivalent support in our quantitative model comparisons. Despite these discrepancies, however, we found that across-condition or across-participant comparisons of metacognitive measures are relatively robust against the distributional assumptions, which provides much assurance to conventional research practice. We hope this article promotes the awareness for the significance of hidden modeling assumptions, contributing to the cumulative development of the relevant field.

Mood symptoms predict COVID-19 pandemic distress but not vice versa: An 18-month longitudinal study.

The COVID-19 pandemic has had medical, economic and behavioral implications on a global scale, with research emerging to indicate that it negatively impacted the population's mental health as well. The current study utilizes longitudinal data to assess whether the pandemic led to an increase in depression and anxiety across participants or whether a diathesis-stress model would be more appropriate. An international group of 218 participants completed measures of depression, anxiety, rumination and distress intolerance at two baselines six months apart as well as during the onset of the COVID-19 pandemic exactly 12 months later. Contrary to expectations, depression, rumination, and distress intolerance were at equivalent levels during the pandemic as they were at baseline. Anxiety was reduced by a trivial degree (d = .10). Furthermore, a comparison of quantitative explanatory models indicated that symptom severity and pandemic-related environmental stressors predicted pandemic-related distress. Pandemic-related distress did not predict symptom severity. These findings underscore the necessity of longitudinal designs and diathesis-stress models in the study of mental health during the COVID-19 pandemic. They also emphasize that individuals with higher rates of baseline psychopathology are as particularly at risk for higher levels of distress in response to disaster-related stressors.

Traffic noise monitoring and modelling - an overview.

Noise has emerged as a leading environmental problem and is an underestimated threat. The most significant source of noise pollution is road traffic. Road traffic noise problem has reached alarming levels. This proves the severity and necessity of mitigating the traffic noise from every delicate corner possible. Noise monitoring is required to check the noise levels and effectiveness of control methods implemented. Road traffic noise control can be exercised with the help of prediction models. This paper presents the traffic noise status of developing countries and a quantitative review and comparison of traffic noise prediction models developed by researchers for various cities. Findings suggest that most of the researchers have used regression modelling and use of evolutionary computing methods like genetic algorithm, fuzzy systems, and neural networks to develop traffic noise prediction model is lacking. The effect of many important variables affecting traffic noise like pavement type, vegetation along roads, road surface roughness, and gradient still needs to be studied. Further, studies are required to measure in vehicle noise levels on same roads to compare the noise levels tolerated by residents, road users, and the commuters; this will help in formulating traffic noise regulations.

Optimization and uncertainty quantification of in situ combustion chemical reaction models

Heavy oil, deep, and waterflooded reservoirs contain significant petroleum resources but present many unique challenges for oil recovery. In situ combustion (ISC) is a thermal enhanced oil recovery method that has been proposed for such reservoirs. A persistent challenge in understanding ISC is modeling the underlying chemical reactions. While many different reaction models have been proposed, these models are typically developed on an ad-hoc basis for individual crude oil samples and manually calibrated to experimental data. While some work exists on applying optimization-based approaches to reaction calibration, existing approaches are not fully-generalized nor do they quantify uncertainty in the estimated parameters. Here we present such an approach for calibrating and quantifying uncertainty in ISC chemical reaction parameters. The workflow presented is generalized to any hydrocarbon pseudocomponent-based reaction model of ISC, provides a framework for quantification of uncertainty in estimated parameters, and is fully automated. Thus, no parameter initialization or manual calibration is needed. We apply this workflow to characterization of a heavy crude oil sample and demonstrate the capability of this workflow to predict ISC oxidation kinetics and upscale to a combustion tube simulation. Overall, this work provides an algorithmic approach to calibrating ISC kinetics that enables quantitative analysis and comparison of oil types, reaction models, and reservoir conditions for ISC.