Interpretation Of Data Research Articles

Fishes in Warming Waters, the Gill-Oxygen Limitation Theory and the Debate Around Mechanistic Growth Models

Mechanistic explanations of the impact of climate change on fish growth are currently under debate. However, critical assessments of even the most prominent theories are not always based on accurate interpretations of their underlying mechanistic models. This contribution addresses some of the major misunderstandings still causing the Gill-Oxygen Limitation Theory (GOLT) from being examined based on its actual structuring elements and assumptions, rather than erroneous perceptions. As we argue, recent critiques of the GOLT are based on implausible interpretations of respirometry data that are invoked to distinguish maintenance costs and overhead costs of growth. Discussing the current state of the debate, we emphasize the fact that fasting young and, thus, growing fish for short periods of time is not sufficient to suppress energy (i.e., oxygen) allocation to growth. In the process of dealing with these issues, several cases of apparent ‘counter-evidence’ are discussed. Highlighting the need to base critical discussions and examinations of the GOLT on its actual predictions, we recommend that testing the theory should focus on broad reviews or meta-analyses, e.g., on datasets of gill surface area and the relationship of these data to growth performance under different temperature regimes.

A comprehensive Beyond-GDP database to accelerate wellbeing, inclusion, and sustainability research

“Beyond-GDP” metrics are essential for understanding societal progress. Yet despite their importance, these metrics are scattered across various databases, hindering accessibility and interdisciplinary analysis. Addressing this gap, we present the ‘WISE database’ – the first extensive collection of important Beyond-GDP metrics organized by Wellbeing, Inclusion, and Sustainability (WISE) dimensions. The WISE database consolidates data from a variety of sources, including international institutions and academic publications. It encompasses over one million data points across 244 metrics, covering 218 countries and 61 country groupings, from specific social and environmental indicators to the main Beyond-GDP indexes, and is augmented by essential metadata. The data primarily spans from 1995 to 2015, with some metrics extending back to the 19th century. To improve accessibility and data interpretation, a user-friendly online visualization tool has been developed. The WISE database aims to foster a broader view of societal progress, facilitate research on synergies and trade-offs of wellbeing, inclusion, and sustainability, and provide a foundation for interdisciplinary research.

Development and validation of a gene expression-based Breast Cancer Purity Score

The prevalence of malignant cells in clinical specimens, or tumour purity, is affected by both intrinsic biological factors and extrinsic sampling bias. Molecular characterization of large clinical cohorts is typically performed on bulk samples; data analysis and interpretation can be biased by tumour purity variability. Transcription-based strategies to estimate tumour purity have been proposed, but no breast cancer specific method is available yet. We interrogated over 6000 expression profiles from 10 breast cancer datasets to develop and validate a 9-gene Breast Cancer Purity Score (BCPS). BCPS outperformed existing methods for estimating tumour content. Adjusting transcriptomic profiles using the BCPS reduces sampling bias and aids data interpretation. BCPS-estimated tumour purity improved prognostication in luminal breast cancer, correlated with pathologic complete response in on-treatment biopsies from triple-negative breast cancer patients undergoing neoadjuvant treatment and effectively stratified the risk of relapse in HER2+ residual disease post-neoadjuvant treatment.

An evaluation of trends for mesothelioma mortality in American women: Addressing the content of a recent Morbidity and Mortality Weekly Report (MMWR).

Mesothelioma is a fatal disease that has historically been associated with exposure to airborne asbestos. Because occupational asbestos exposures dropped dramatically in the late 1960s and early 1970s, far fewer cases of mesothelioma today are due to these fibers but, instead, are usually a result of the aging process or genetic predisposition. In May of 2022, a Morbidity and Mortality Weekly Report (MMWR) was issued by the Centers for Disease Control and Prevention (CDC) regarding malignant mesothelioma incidence in women from 1999 to 2020. While this MMWR alerted citizens to the continued presence of the disease, after reading this article one might have thought that the CDC was suggesting that the disease was increasing in women due to asbestos exposures (which it is not). In the present analysis, we investigate several factors related to the interpretation of epidemiological data for mesothelioma, including the role of asbestos as a risk factor over time. The authors conducted a review of the scientific community's understanding of mesothelioma incidence and asbestos exposures amongst women, as well as an investigation of the methods and references in the MMWR article. Although various articles have recently discussed the incidence of both peritoneal and pleural mesothelioma in women, it is fortunate that the age-adjusted rates for mesothelioma have remained flat (neither increased nor decreased significantly) in women for the past 50 years. Incredibly few women in the U. S. have had appreciable cumulative exposures to any type of asbestos (chrysotile, amosite, or crocidolite) in the workplace or from the ambient environment, especially since about 1965-1970. In this paper, we highlight six factors that should be considered when evaluating the incidence of mesothelioma amongst American women in the current era. Without sufficient consideration of these factors, improper conclusions have been drawn over the past several years.

Rapid advancements in large language models for quantitative remote sensing: The case of water depth inversion

This study presents a comparative analysis of two advanced AI models, ChatGPT and ERNIE, in the context of water depth inversion. Utilizing satellite spectral data and in-situ bathymetric measurements collected from Rushikonda Beach, India, we processed and analyzed the data to generate high-resolution bathymetric maps. Both models demonstrated significant accuracy, with ChatGPT slightly outperforming ERNIE in terms of mean absolute error. The study highlights the advantages of AI models, such as efficient data processing and the ability to integrate multi-modal inputs, while also discussing challenges related to data quality, interpretability, and computational demands. The findings suggest that while both models are highly effective for water depth inversion, ongoing improvements in data handling and model transparency are essential for their broader application in environmental monitoring. This research contributes to the understanding of AI capabilities in geospatial analysis and sets the stage for future enhancements in the field.

Oscillometric method: what is it and is it suitable for measuring blood pressure in all patients?

The oscillometric method, which is gradually replacing the traditional auscultatory method, is attractive to practitioner due to the possibility of automating blood pressure (BP) measurements. However, there is little literature information about its limitations. The purpose of this review is to address such issues. For this purpose, the main modern information on the maximum amplitude algorithm for BP measurement and location features, in which it is possible to obtain an oscillometric waveform, are preliminarily considered. In addition, the limitations and disadvantages of the method associated with both the equipment and the characteristics of various population groups are analyzed. The list of groups in which blood pressure measuring devices may be inaccurate has expanded significantly in the 2020s compared to the previous decade. This suggests that the reliability of the interpretation of data obtained by the oscillometric method in a certain part of patients is limited.

Ent-Abietane-type lactones with anti-inflammatory activity from Euphorbia helioscopia

Euphohelinodes D–I (1–6), six previously unreported ent-abietane lactones, along with two known analogues (7 and 8), were isolated from the anti-inflammatory fraction extracted from E. helioscopia by a bioactivity-guided isolation. Their structures were characterized using a combination of spectroscopic data interpretation, single-crystal X-ray diffraction and ECD analysis. The anti-inflammatory activity of these compounds was evaluated by measuring their inhibitory effects on NO production in LPS-stimulated RAW264.7 macrophages. The most active candidate, euphohelinode H (5), had better inhibitory activity against NO production with an IC50 value of 30.23 ± 2.33 μM. Further study revealed that 5 significantly suppressed the expressions of iNOS and COX-2 through the NF-κB signaling pathway.

Underpowered studies in muscle metabolism research: Determinants and considerations

Biomedical research frequently employs null hypothesis testing to determine whether an observed difference in a sample is likely to exist in the broader population. Null hypothesis testing generally assumes that differences between groups or interventions are non-existent, unless proven otherwise. Because biomedical studies with human subjects are often limited by financial and logistical resources, they tend to have low statistical power, i.e. a low probability of statistically confirming a true difference. As a result, small but potentially clinically important differences may be overseen or ignored simply due to the absence of a statistically significant difference. This absence is often misinterpreted as ‘equivalence’ of treatments. In this educational paper, we will use practical examples related to the effects of exercise and nutrition on muscle protein metabolism to illustrate the most important determinants of statistical power, as well as their implications for both investigators and readers of scientific articles.Changes in muscle mass occur at a relatively slow rate, making it practically challenging to detect differences between treatment groups in a long-term setting. One way to make it ‘easier’ to differentiate between groups and hence increase statistical power is to have a sufficiently long study duration to allow treatment effects to become apparent. This is especially relevant when comparing treatments with relatively small expected differences such as the effect of modest changes in daily protein intake. Secondly, one could try to minimize the variance and response heterogeneity within groups, for example by using strict inclusion criteria and standardization protocols (e.g., meal provision), by using cross-over designs, or even within-subject designs where two interventions are compared simultaneously (e.g., studying an exercised limb vs a contralateral control limb) although this might limit the generalizability of the findings (e.g. such single-limb exercise training is not common in practice). In terms of data interpretation, investigators should obviously refrain from drawing strong conclusions from underpowered studies. Yet, such studies still provide valuable data for meta-analyses. Finally, because muscle protein synthesis rates are highly responsive to anabolic stimuli, acute metabolic studies are more sensitive to detect potentially clinically relevant differences in the anabolic response between treatments. Apart from further elaborating on these topics, this educational article encourages readers to more critically question null findings and scientists to more clearly discuss limitations that may have compromised statistical power.

Ellen Churchill Semple: Navigating a Patriarchal Discipline

Ellen Churchill Semple is a notorious figure in the geographical realm whose complicated legacy renders her place within a historiography of geography uncertain. While Semple is hailed as the first female geographer, it is also widely acknowledged that her work is tainted with racism. Contemporary scholars wish to erase her contributions, declaring that they would “prefer to forget” her infamous text Influences of Geographic Environment in which she claimed that physical geography determined human behaviour. Mona Domosh complicates this desire to erase Semple from memory by advocating for a feminist historiography of geography. Through foregrounding the contributions that women have made to the development of geographic knowledge, Domosh challenges the exclusion of women from accounts of the discipline’s past. This paper therefore addresses the question: Should Semple be included in a feminist historiography of geography? Furthermore, was Semple a feminist? To interrogate this, I reimagine Pearson’s take on violent women. Just as Atwood describes a woman’s disbelief at her ability to cause harm in the opening quote, Pearson centres the misperception that women are simply compassionate caregivers and victims of their circumstances – the ‘myth of female innocence’. This is useful for exploring Semple’s role in epistemological violence where her data interpretations brought negative consequences for certain groups. An intersectional lens is crucial for examining the connections between race, class, and gender which shaped the experiences of Semple and those she deemed ‘inferior’. The structure-agency debate emerges, asking whether individuals are entirely socialised by structures or can act autonomously. I take a relational approach to acknowledge the interrelatedness of social processes and argue that Semple both actively challenged and perpetuated patriarchy. Firstly, this paper outlines Semple’s theoretical contributions and other key insights provided by contemporary female geographers which are useful for assessing Semple’s impact today. Secondly, Semple’s infiltration of male-dominated space is examined to demonstrate her resistance to traditional gender expectations. Thirdly, her imperial entanglements are traced to illustrate her perpetuation of the patriarchal state. Finally, a contemporary manifestation of the Semple dilemma is considered. I conclude that by today’s standards of intersectional feminism Semple is not a feminist role model, however she can be considered an early feminist for her active reconfiguration of an all-male discipline. Ultimately, I advocate for the critical inclusion of Semple’s work in a feminist historiography of geography. This project of decoding knowledge to understand the influence of cultural forces offers a step towards decolonising the academy.

A Comprehensive Geophysical Exploration of Sedimentary Exhalative Deposits: An Example from the Huaniushan Lead–Zinc–Silver Polymetallic Deposit in Gansu, China

The Huaniushan lead–zinc–silver deposit is a hydrothermal sedimentary exhalative deposit (SEDEX), and the mining area has complex geological conditions, with the main tectonic structure being the Huaheitan–Shuangfengshan Fault (F3), which controls the distribution of strata and magmatic rocks. Since the discovery of the Huaniushan lead–zinc–silver deposit, diverse interpretations of its genesis and metallogeny have been proposed, making it challenging to establish a definitive geological explanation. Moreover, using a single geophysical exploration method relies on limited rock physical parameters, making it difficult to effectively characterize underground structures. The combined use of multiple geophysical methods can effectively integrate the geophysical characteristics of different rock physical parameters, reducing the multiplicity and uncertainty of the inverse interpretation of geophysical data. The comprehensive interpretation of three-dimensional inversion based on various geophysical data, the construction of geological–geophysical models on geological grounds, the establishment of hidden ore exploration and positioning, and the rapid evaluation of geophysical technological systems are the current research trends in mineral exploration. In light of this, in this study, we conducted research on the three-dimensional inversion interpretation of gravity and magnetoelectric exploration data of the Huaniushan sedimentary exhalative lead–zinc–silver polymetallic deposit and constructed a three-dimensional geological–geophysical model of the study area based on the obtained three-dimensional physical structure of the underground density, magnetization intensity, resistivity, and polarizability of the study area, in combination with related geological and drilling hole data. Finally, we comprehensively interpreted the favorable mineralization sites in the study area.

Exploring the Impact of Sex and Gender in Brain Function: Implications and Considerations.

Sex and gender are crucial variables in understanding brain development and disease. Biological sex is determined by genetic and hormonal factors, whereas gender is a multidimensional construct shaped by social and cultural influences. The interplay of these factors contributes to sex-specific susceptibilities and disease progression in psychiatric and neurological disorders. However, sex and gender are often considered as a single variable, which can lead to biased data analysis and interpretation. This commentary aims to analyze how sex and gender influence brain structure and function, with implications for personalized medicine, research, and the development of gender-sensitive clinical guidelines. Findings from various studies employing neuroimaging techniques and animal models are discussed, as well as the impact of biological sex, gender, environmental, cultural, and social factors on brain development, organization, and behavior. Evidence suggests that sex differences in brain structure and function are not only genetically determined but arealso influenced by gender-related experiences and societal contexts. Importantly, discrepancies between male and female brains are reduced in gender-equal societies. Preclinical studies play a pivotal role in determining the influence of biological sex, independent of gender, in different disease models. The findings underscore the need to consider both sex and gender in research and clinical practice to avoid biases and promote equitable health outcomes. Moving forward, we advocate for gender-sensitive approaches to be integrated into brain research and in clinical guidelines to achieve personalized and precision medicine.

Epidemiological Insights into Diabetes, Hypertension, and Cardiovascular Diseases: Implications for Public Health Nutrition

This review examines the epidemiological understanding of diabetes, hypertension, and cardiovascular diseases as interconnected nutritional disorders. These conditions, sharing common dietary and lifestyle risk factors, collectively represent significant global public health challenges that require integrated investigation and intervention approaches. Epidemiological approaches, including descriptive, analytical, and interventional designs, emphasizing their strengths and limitations in investigating disease prevalence, risk factors, and potential interventions. It scrutinizes the utilisation of routine population data and surveillance systems in estimating disease burden and assessing intervention effectiveness, focusing on large-scale population-based studies and national surveys. Advanced statistical analyses enhance data interpretation while accounting for demographic variables, ensuring precise comparative analyses across populations. The application of standardization techniques and robust statistical methods strengthens epidemiological findings and minimizes potential biases. A coordinated, multidisciplinary approach is essential for translating epidemiological evidence into effective public health interventions, emphasizing collaboration between epidemiologists, healthcare practitioners, and policymakers. This analysis provides insights for researchers, public health professionals, and policymakers engaged in addressing nutrition-related chronic diseases through epidemiological approaches, highlighting the field's potential to mitigate the escalating burden of these conditions while acknowledging its limitations.

Gamma Dose Rate Measurements in Northern Spain: Influence of Local Meteorological Scenarios on Radiological "False Alarms" in a Real-Time Radiological Monitoring Network.

The present study characterizes gamma dose rate (GDR) concentrations in Bilbao, located in the northern Iberian Peninsula, utilizing a comprehensive 10-min interval database spanning from 2009 to 2018. This station belongs to the radiological environmental monitoring of the Basque Country network. The daily average GDR was found to be 0.07624 ± 0.00004 µSv/h, with the daily 95th percentile averaging 0.08026 ± 0.00007 µSv/h throughout the entire period. Our analysis specifically addresses the impact of precipitation on GDR, revealing a higher correlation coefficient for daily 95th percentile values compared to daily averages. Additionally, the influence of the Galerna (GL) event, a natural meteorological phenomenon in this region, on GDR was investigated, noting that it can develop both with and without precipitation. Understanding the interaction between GDR and this meteorological scenario is vital for the development of more reliable radiological monitoring strategies and for safeguarding public health. For this purpose, 40 GL events were analyzed. The present results indicate that GDR values frequently exceed alarm levels when a GL is formed. These GDR peaks should be considered natural radiological events, necessitating the classification of such GDR peaks as false alarms within the radiological monitoring network. To explain them in detail, 10-min time series of precipitation and radon outdoor concentrations were analyzed. The results demonstrate that the GL event with precipitation is a meteorological scenario that can be associated with false alarms. The present analysis provides a distinct contrast in radon behavior under the same meteorological event in case of being developed with precipitation or without precipitation. The findings from this analysis are crucial for avoiding and understanding false radiological alarms triggered in the monitoring network, thereby enhancing the accuracy of radiological data interpretation and improving public safety measures.

Application of Advanced Microscopy Techniques to the Characterization of Mixed Matrix Membranes.

Mixed matrix membranes (MMMs) have emerged as promising materials for various separation processes due to their tunable properties, enhanced separation performance and reproducibility. In this review paper, we provide a comprehensive overview of the methodologies, challenges, and applications associated with the characterization of MMMs using two advanced imaging techniques: Focused Ion Beam Scanning Electron Microscopy (FIB-SEM) and Transmission Electron Microscopy (TEM). We begin by outlining the principles and capabilities of FIB-SEM and TEM, emphasizing their suitability for studying the microstructure, morphology, and composition of MMMs at nanoscale resolution. Subsequently, we discuss the specific challenges and limitations encountered in the characterization of MMMs using these techniques, including sample preparation, image acquisition, and data interpretation. Furthermore, we review the diverse applications of FIB-SEM and TEM in elucidating the structure-property relationships of MMMs. Through illustrative examples, we highlight the valuable insights gained from these imaging techniques in optimizing MMMs for various separation applications. Finally, we propose future directions and emerging trends in MMM characterization, including the integration of lasers into FIB-SEM and in situ characterization techniques, to address current challenges and push the boundaries of MMM design and performance. Overall, this review provides a comprehensive overview of the state-of-the-art methodologies for characterizing MMMs using FIB-SEM and TEM, identifies key challenges, and offers insights into future research directions aimed at harnessing the full potential of MMMs for sustainable separation technologies.

Application of intelligent self-organizing algorithms in UAV cooperative inspection of power distribution networks

In the rapidly evolving technological landscape, the advent of collaborative Unmanned Aerial Vehicle (UAV) inspections represents a revolutionary leap forward in the monitoring and maintenance of power distribution networks. This innovative approach harnesses the synergy of UAVs working together, marking a significant milestone in enhancing the reliability and efficiency of infrastructure management. Despite its promise, current research in this domain frequently grapples with challenges related to efficient coordination, data processing, and adaptive decision-making under complex and dynamic conditions. Intelligent self-organizing algorithms emerge as pivotal in addressing these gaps, offering sophisticated methods to enhance the autonomy, efficiency, and reliability of UAV collaborative inspections. In response to these challenges, we propose the MARL-SOM-GNNs network model, an innovative integration of Multi-Agent Reinforcement Learning, Self-Organizing Maps, and Graph Neural Networks, designed to optimize UAV cooperative behavior, data interpretation, and network analysis. Experimental results demonstrate that our model significantly outperforms existing approaches in terms of inspection accuracy, operational efficiency, and adaptability to environmental changes. The significance of our research lies in its potential to revolutionize the way power distribution networks are inspected and maintained, paving the way for more resilient and intelligent infrastructure systems. By leveraging the capabilities of MARL for dynamic decision-making, SOM for efficient data clustering, and GNNs for intricate network topology understanding, our model not only addresses current shortcomings in UAV collaborative inspection strategies but also sets a new benchmark for future developments in autonomous infrastructure monitoring, highlighting the crucial role of intelligent algorithms in advancing UAV technologies.

Requirements to the position of a financial manager in a modern commercial organization

The article is devoted to the study and identification of new competencies for the position of a financial manager, which an employee in a modern commercial organization must have, as well as the analysis of the requirements that companies impose on financial managers. It considers the role and features of the position of a financial manager in a commercial organization, determines his plays in the job hierarchy of the company. Particular attention is paid to the influence of digitalization and technologies based on artificial intelligence on the work of financial managers in a commercial organization. The article uses such research methods as analysis, synthesis, modeling, observation and comparison, as well as methods of data processing and interpretation. The relevance of the work lies in increasing the interest of financial managers in digital technologies and ways to increase the company’s profits thanks to modern tools. Scientific novelty of the study consists of identifying the relationship between the degree of use of mechanisms based on artificial intelligence in the work of a financial manager with the effectiveness of his activities. The work provides recommendations for changing the professional competencies for a candidate for the position of financial manager in a modern commercial organization. The scientific study was conducted in relation to large companies in various industries and based on analysis of the labor market and job search services.

Neurophysiological approaches to exploring emotional responses to cosmetics: a systematic review of the literature.

This systematic review explores the use of neurophysiological measurements to study emotional responses to cosmetic products. The aim is to evaluate existing literature on these measurements in cosmetics, identify the main findings, highlight methodological challenges, and propose new guidelines for future research. A systematic search focusing on neurophysiological measures to determine emotions induced by different cosmetic products was carried out in accordance with PRISMA guidelines. A total of 33 articles identified with the EBSCO database met the inclusion criteria. In all, 10 different measurement tools were used in these articles to assess the emotional effects of cosmetic products. This review emphasizes the complexity of interactions between cosmetics and emotional responses. It underscores the importance of future research with a more holistic approach that couples several physiological measurements. Among them, electrophysiological brain activity shows potential for enhancing understanding of emotional responses related to cosmetic products. Frontal asymmetry, particularly in the alpha frequency band, was often use and frequently linked to positive emotional states, although conflicting evidence exists. Additionally, cardiac activity, specifically the LF/HF ratio, emerges as a promising marker for differentiating between different cosmetic products. However, methodological heterogeneity, present challenges for replicability, generalizability, and complicate data interpretation.

The dynamics of protest participation patterns: analysis of the Polish case

ABSTRACT A variety of research techniques, including survey research, are used to estimate the participation turnout in nonviolent protests. The issues with survey data interpretation, as well as any discrepancies and emerging research methodologies, are illustrated using Poland as an example. The problems of comparison and measurement that have been identified include: (1) non-random distribution of activism in the population, (2) variability of the pattern over time (dynamics of contention), and (3) sampling and format of questions in surveys. A comparison with the protest cultures of other European countries highlights that Poland has some of the lowest average values of declared engagement in demonstrations. However, it also points to a significant dynamic increase in 2022 and differences in declaration levels during the studied period (2006–2022). The obtained results based on Polish national survey (CBOS) and European Social Survey indicate a rising trend in recent years in Poland, with a straightforward estimation suggesting an annual average participation of around 1.8 million adult citizens in street demonstrations from 2016 to 2022. Research conducted within the ESS indicates higher values for the same period, at around 2.6 million.

A comparison of electromyography techniques: surface versus intramuscular recording.

This review is a comprehensive guide for electromyography (EMG) researchers, providing a comparison of skin EMG recording (surface EMG: sEMG and high-density sEMG: HD-sEMG) and intramuscular EMG recording (multi-motor unit-MMU and single motor unit electromyography-SMU). We delve into the nuances of techniques, highlighting their strengths and limitations in quantifying muscle activation during dynamic and static conditions. We first examine how EMG signals change with time, focussing on the interplay between motor unit synchronisation and signal amplitude. The review then explores the impact of electrode placement on signal quality. We further discuss the challenges of signal cancellation, crosstalk from neighbouring muscles, and motion artifacts, which can significantly affect signal integrity. Finally, we address the temporal changes in electrode impedance and its implications for data interpretation. Our analysis proposes that specific research objectives should guide the choice amongst sEMG, HD-sEMG, SMU and MMU. MMU, which records the peak counts of individual motor unit action potentials from a localised muscle area, is particularly suited for studying deep or small muscles during static and dynamic activities. Its high sensitivity to motor unit recruitment and discharge rates minimises the impact of factors such as signal cancellation and motion artefacts. Conversely, sEMG is well-suited for short-duration, isometric assessments of large, superficial muscles. HD-sEMG helps study single motor unit properties under isometric conditions. SMU is particularly suited for studying neuronal networks between stimulated sites and motor neurons. This review aims toprovideresearchers with the information to select the most appropriate EMG technique for their investigations.

The solution of the task of dynamic interpretation of seismic data using machine learning methods

This article examines the problem of dynamically interpreting seismic data using machine learning models, which include Extremely Randomized Trees (Extra Trees), Gradient Boosting (GB), and Adaptive Boosting (AdaBoost) for the given problem. The study analyzes some existing solutions of the problem and describes the advantages of these machine learning models. Accuracy is estimated using the root mean square error metric. The authors found that dynamic interpretation and prediction of seismic data using these machine learning methods had not been extensively explored in research on related topics, which became the main focus of the study. The article formalizes the use of the mentioned models and highlights features and advantages for the given problem. Several common machine learning methods were investigated to find functional relationships between input parameters. Computational experiments were conducted to evaluate their applicability and compare the algorithms. The results show that the Extra Trees method is the most suitable for practical use for the given problem, as it demonstrates the highest accuracy in determining functional relationships and dynamic interpretation.