Separate Classes Research Articles

Genome-wide association identifies genomic regions influencing fillet color in Northwest Atlantic salmon (Salmo salar Linnaeus 1758).

Atlantic salmon (Salmo salar) is an important source of food globally; however, fillet color can significantly affect consumer purchasing, leading to potential food waste. Fish diets can be supplemented with astaxanthin to increase the organic pigment, carotenoid, responsible for flesh coloration; however, there is variation in the amount of overall fillet coloration in response to feeding astaxanthin. The uptake of this pigment is influenced by the environment and genetics and has been shown to be heritable. Therefore, we set out to determine the genomic associations of two separate year classes of farmed North American Atlantic salmon with measured Minolta Chroma Meter (lightness, redness, and yellowness) and SalmoFan phenotypic traits. Using ASReml-R genome-wide association, two genetic markers on chromosome 26 were significantly associated with almost all color traits, and these two markers explained between 6.0% and 12.5% of the variances. The genomic region on chromosome 26 was importantly found to be associated with the beta-carotene oxygenase 1 (bco1) gene, which is essential in the conversion of beta-carotenoids to vitamin A, implying that this gene may also play an important role in flesh coloration in North American Atlantic salmon. Additionally, there were several genomic regions significantly associated with color traits, in which the accompanying genes had functions in line with thermogenesis, immune function, and pathogenic responses. Understanding how environmental and genetic factors work together to affect fillet quality traits will help inform genetic improvement.

Worst-Case Discriminative Feature Learning via Max-Min Ratio Analysis.

We propose a novel discriminative feature learning method via Max-Min Ratio Analysis (MMRA) for exclusively dealing with the long-standing "worst-case class separation" problem. Existing technologies simply consider maximizing the minimal pairwise distance on all class pairs in the low-dimensional subspace, which is unable to separate overlapped classes entirely especially when the distribution of samples within same class is diverging. We propose a new criterion, i.e., Max-Min Ratio Analysis (MMRA) that focuses on maximizing the minimal ratio value of between-class and within-class scatter to extremely enlarge the separability on the overlapped pairwise classes. Furthermore, we develop two novel discriminative feature learning models for dimensionality reduction and metric learning based on our MMRA criterion. However, solving such a non-smooth non-convex max-min ratio problem is challenging. As an important theoretical contribution in this paper, we systematically derive an alternative iterative algorithm based on a general max-min ratio optimization framework to solve a general max-min ratio problem with rigorous proofs of convergence. More importantly, we also present another solver based on bisection search strategy to solve the SDP problem efficiently. To evaluate the effectiveness of proposed methods, we conduct extensive pattern classification and image retrieval experiments on several artificial datasets and real-world ScRNA-seq datasets, and experimental results demonstrate the effectiveness of proposed methods.

Effect of Simulation based teaching strategy in Engineering Physics course

Computer-based simulations have gained significant popularity in science education, particularly in subjects like Physics that involve intricate and abstract concepts. Simulations offer learners the opportunity to engage with complex phenomena and manipulate them, making them particularly valuable in these contexts. In line with this notion, our study adopted a teaching strategy centered on simulations. This approach was implemented in two separate classes of first-year Bachelor of Technology (F. Y. B. Tech) students, specifically for the Engineering Physics course. In one of these classes, simulations were solely utilized without incorporating any formative assessment, while the other class was exposed to a strategy that combined simulations with formative assessment. Our observations revealed that solely using simulations during instruction did not contribute significantly to conceptual understanding. However, when simulations were integrated with formative assessment, a substantial improvement in understanding was noted. Through our data analysis, a noteworthy discrepancy in average scores between the two classes became apparent. Notably, the class that experienced the simulation-based formative assessment strategy demonstrated a higher average score of 63.85. Keywords—Enter three to six keywords or phrases in alphabetical order, separated by semi-colons. JEET Category—Choose one: Research,

Spectral uncertainty analysis of grassland and its camouflage materials based on land-based hyperspectral images

Abstract Spectral uncertainty is one of the most prominent spectral characteristics of hyperspectral images. Compared to the process of remote sensing hyperspectral imaging, hyperspectral imaging under land-based imaging conditions has the characteristics of variable detection directions, random imaging times, and complex environmental conditions, resulting in increased spectral uncertainty of targets in land-based hyperspectral images. The spectral uncertainty of the target mainly refers to the phenomenon of “Same spectral reflectance but different objects” and “Same object but different spectral reflectance” which poses significant difficulties for subsequent hyperspectral image target detection and recognition. In order to analyze the spectral uncertainty of hyperspectral images in land-based applications and address the issue of spectral uncertainty in similar targets, a spectral uncertainty evaluation index based on standard deviation vector was proposed. For the overall spectral separability between different types of targets, a quantitative index based on Jaccard Distance (JD-SSI) is proposed to measure the spectral separability of different kinds of targets. The experiment focused on grassland and its four typical camouflage materials, analyzing the spectral intra class differences and inter class separability of each target with grassland. It is a fundamental work for studying the spectral characteristics of land-based hyperspectral images, providing a new approach for subsequent spectral band extraction, hyperspectral image classification, and target detection tasks.

A dynamic traffic assignment framework for policy analysis in cities with significant share of two-wheelers

High maneuverability of motorized two-wheelers amidst vehicles of bigger size and different dynamics invalidates FIFO to traverse through the gaps between other vehicles for faster mobility. The failure of existing dynamic traffic assignment frameworks with multi-class conditions to capture this behaviour results in inaccurate routing. The study proposes a simulation based two-class dynamic traffic assignment framework comprising of two-wheeler specific behaviour. These features when incorporated in the framework will add to the utility of the traditional dynamic traffic assignment framework in travel time prediction and planning level applications and is therefore relevant to regions with significant share of two-wheelers. The study gives a clear view of the effect of two-wheeler specific features on the route choice behaviour based on the dynamic travel time. The results of the study shows that there occurs an unintentional separation of vehicle classes during congestion; this effect can be utilized for a two-wheeler specific policy implication for congestion management in cities. The proposed framework can be employed in identifying the optimal provision of exclusive two-wheeler lanes. It is also observed that the provision of exclusive lanes may sometimes be counterproductive.

Perception of Parents about Female Education: A Case study of the University of Loralai

The study sought to explore parents' perceptions of female education. To determine the social and cultural hurdles in female education. The University of Lorelai’s students comprised the population of this research. A questionnaire was distributed to the female students at random. From the whole population, the current researcher took 50 girls as a sample for this research from the University of Loralai, comprising the Department of Education. This sample was selected through a random sampling technique. A closed-ended questionnaire was constructed to collect data containing ten items based on a 3-point scale. Based on the findings, it was concluded that early marriages are a barrier to female education. Respondents are divided in their opinion about the statement that parents think co-education is difficult for women. Most parents cannot afford higher education for their daughters. Most parents believe that higher education is not difficult for women. Most parents believe that they cannot afford higher education for their daughters. Local tribal tradition is a barrier to female education. Parents agree that the university is too far away from the city. Respondents are divided in their opinion about the statement that parents are not satisfied with transport facilities. If the university provides separate classes for female students, the strength of female students will increase. Most parents are worried about harassment.

GHA-DenseNet prediction and diagnosis of malignancy in femoral bone tumors using magnetic resonance imaging

Background and objectiveDue to their aggressive nature and poor prognosis, malignant femoral bone tumors present considerable hurdles. Early treatment commencement is essential for enhancing vital and practical outcomes. In this investigation, deep learning algorithms will be used to analyze magnetic resonance imaging (MRI) data to identify bone tumors that are malignant. MethodologyThe study cohort included 44 patients, with ages ranging from 17 to 78 (22 women and 22 males). To categorize T1 and T2 weighted MRI data, this paper presents an improved DenseNet network model for the classification of bone tumor MRI, which is named GHA-DenseNet. Based on the original DenseNet model, the attention module is added to solve the problem that the deep convolutional model can reduce the loss of key features when capturing the location and content information of femoral bone tumor tissue due to the limitation of local receptive field. In addition, the sparse connection mode is used to prune the connection mode of the original model, so as to remove unnecessary and retain more useful fast connection mode, and alleviate the overfitting problem caused by small dataset size and image characteristics. In a clinical model designed to anticipate tumor malignancy, the utilization of T1 and T2 classifier output values, in combination with patient-specific clinical information, was a crucial component. ResultsThe T1 classifier's accuracy during the training phase was 92.88% whereas the T2 classifier's accuracy was 87.03%. Both classifiers demonstrated accuracy of 95.24% throughout the validation phase. During training and validation, the clinical model's accuracy was 82.17% and 81.51%, respectively. The clinical model's receiver operating characteristic (ROC) curve demonstrated its capacity to separate classes. ConclusionsThe proposed method does not require manual segmentation of MRI scans because it makes use of pretrained deep learning classifiers. These algorithms have the ability to predict tumor malignancy and shorten the diagnostic and therapeutic turnaround times. Although the procedure only needs a little amount of radiologists' involvement, more testing on a larger patient cohort is required to confirm its efficacy.

CSViz: Class Separability Visualization for high-dimensional datasets

CSViz: Class Separability Visualization for high-dimensional datasets

HOSPICE USE AND END-OF-LIFE EXPERIENCE BY RACE AND GEOGRAPHY: A LATENT CLASS ANALYSIS

Abstract Long existing inequalities near the end-of-life (EOL), including the death experience, have been uncovered because of increased attention during the COVID-19 pandemic. Hospice may be an avenue to equalize EOL experiences. This study aimed to 1) understand the profiles of hospice use and EOL experience in a national representative sample of decedents and 2) uncover how race and/or geographic location predict class membership. We conducted a latent class analysis with covariates using Waves 9-11 (n=1050) from the last month of life interview in the National Health and Aging Trends Study. Based on fit statistics, response probabilities, class separation, interpretability, and fit with theory, a four-class model was ultimately selected. The four latent classes were: well-rounded hospice care; no hospice, goal concordant care; no hospice, poor overall care; and complex hospice care. We found that geographic location was not a significant predictor of latent class membership but being non-White was significantly associated (p < .001) with 8.79 times higher odds of being in the no hospice, poor overall care group and 1.85 times higher odds of being in the no hospice, complex care group compared to the reference class (well-rounded hospice care). The current study’s findings paint a complex picture of hospice use and EOL experience in a nationally representative sample. Hospice use alone does not make a “good death” experience, particularly for people of color. Rather, there are unique profiles of hospice use and experiences at EOL. Future research should explore the impact of geographic location on the death experience.

A powerful two-dimensional chromatography method for the non-target analysis of depolymerised lignin

BackgroundLignin is an abundant natural polymer obtained as a by-product from the fractionation of lignocellulosic biomass. In the name of a circular economy, lignin should be valorised into valuable chemicals or biomaterials and utilised instead of petrochemicals. For the development of efficient valorisation processes, the structural characterisation of lignin can be highly beneficial. However, this is an arduous task, as the isolated (and sometimes processed) lignin mainly consists of various neutral monomers but also oligomers. In addition, the material contains isomers, which can be especially problematic to separate and identify. ResultsWe present a powerful off-line comprehensive two-dimensional (2D) chromatography method combining liquid chromatography (LC), supercritical fluid chromatography (SFC), and high-resolution mass spectrometry for the non-target analysis of depolymerised lignin. The implementation of a 1-aminoanthracene column in the second dimension enabled a class separation of potential lignin monomers, dimers, trimers, and tetramers with an additional separation based on the number of hydroxyl groups and steric effects. The pentafluorophenyl column in the first dimension additionally improved the separation based on hydrophobicity. The comparison of off-line 2D LC × SFC to 1D SFC showed that besides the overall improved performance, the first method is also superior for the separation of isomers. Advanced data analysis methods (MS-DIAL, SIRIUS, and Feature-Based Molecular Network) were integrated into the non-target workflow to rapidly visualise and study the detected compounds, which proved to be especially beneficial for the characterisation of the separated isomers. SignificanceThe method yielded the first 2D LC plot demonstrating a classification of lignin compounds, which can be applied to compare various lignin sources and processing methods. In addition, the technique demonstrated improved separation of compounds, including isomers, which was especially beneficial as 77 % of the detected compounds had at least one isomer in the same lignin sample.

Variatsionnye metody resheniya zadach, svyazannykh s iskusstvennym intellektom

We introduce the concepts of a learning algorithm, an objective function, a recognition system, a class of patterns, a training set, a reward algorithm, a finitely convergent algorithm, an adaptive control system, a control goal, control tactics, adaptation time, etc., related to the problem of artificial intelligence in the processes of learning and adaptation. The general problem of self-learning (unsupervised learning)—about the separation of sets—in terms of the classical calculus of variations is posed. The generality of the problem is due to the introduction of an additional time variable into the analysis. The problem is solved by determining extremal conditions under which the minimization of the overall average risk functional is achieved. Problems corresponding to nonfixed and fixed time intervals are considered. For these two cases, expressions are found for calculating variations in cost functionals. Necessary conditions are indicated for determining the extremal values of the self-learning process (separation of classes of a set of patterns) in time.

Developing Futures Thinking Literacy Among Middle School Gifted and Regular Students

ABSTRACT The current study examined the effects of an intervention developing Futures Thinking Literacy (FTL) among middle school gifted and regular students. Study participants (N = 877) were divided into gifted students studying in separate classes, gifted students studying in pullout centers, and regular students. The quantitative research applied a pre-post 6 by 2 design, when the research group experienced an intervention using the multidimensional curriculum model and the control group studied traditionally. A validated FTL questionnaire applied in the study was comprised of two open questions and 50 statements relating to the cognitive, and social-emotional aspect of FTL. Results indicate that both gifted and regular students from the research group improved their futures thinking and overall FTL. Students from gifted classes preferred more challenging activities, a better learning experience, and more knowledge acquisition. Implications regarding teacher preparation in the field of gifted education are discussed.

Spatial identification of lost-and-found carbon hotspots at Javanese rice systems, Indonesia

CONTEXTAgricultural landscapes play a crucial role in global carbon sequestration and biodiversity conservation. The presence of trees outside forests has been recognized as a significant carbon pool, but it is crucial to acknowledge that these trees serve purposes beyond being a carbon sink. This study focuses on integrating border trees and home gardens as distinct land use classes in Indonesian Javanese agricultural land to assess their impact on carbon stock potential and biodiversity connections. OBJECTIVEThe objective is to classify and quantify the tree composition of these classes, estimate their carbon stock potential, analyze plant species diversity, and identify the ecological connections between home gardens and field borders. METHODSTo achieve this, high-resolution multispectral and Synthetic Aperture Radar images were used from four sampling areas covering 100 km2 each in Lamongan and Malang districts, East Java, Indonesia, for land cover/land use mapping. The land use function method was used to identify border trees and home gardens as separate land use classes. Carbon stock potential was estimated based on tree cover composition, while plant species diversity was analyzed through field surveys and farmer focus group discussions. RESULTS AND CONCLUSIONSCrops with border trees cover approximately 28% to 48% of the cropland area in the study sites, while home gardens represent about 74% to 85% of the settlement area. This suggests a potential to double tree presence in these sites by adopting border trees and home gardens practices across the entire area. The median tree cover ratio was around 27% for border trees and 33% for home gardens. This stratification approach identified 31% to 50% more carbon stock compared to calculations using standard classes. Furthermore, home gardens and border trees show a positive correlation with species richness, indicating their role as keepers of biodiversity. Therefore, home gardens and border trees could serve as a sustainability indicator for agricultural land in the Javanese farm system. SIGNIFICANCEThis research contributes to sustainable agricultural practices and climate change mitigation. The stratification approach of identifying border trees and home gardens offers a cost-effective approach for the better computation of carbon stock and can lead to effective land use policies and conservation measures that promote ecological resilience. The study provides valuable insights for similar agricultural contexts worldwide, guiding efforts toward a more sustainable agricultural future.

ШКІЛЬНА ОСВІТА ДІТЕЙ-РОМІВ У ЗАКАРПАТТІ: ІСТОРІЯ СТАНОВЛЕННЯ ТА РОЗВИТКУ

The Roma population has always been an integral part of the demographic diversity of Transcarpathia. However, representatives of this particular national community are traditionally the least involved in educational processes. Given the rights and opportunities for equal access to education for all citizens, regardless of their nationality, as well as the need to integrate the Roma population into civil society, the problem of involving Roma children in education, which depends on a number of conditions (social, economic, ideological, etc.), is relevant. The issue of "harming" Roma children in Transcarpathia at the state level began to be addressed in the early twentieth century, both through legal regulation and by establishing the first school for Roma children in Uzhhorod in 1926. Purpose: article is devoted to the study of the history of the formation and development of school education of Roma children in Transcarpathia. The methods are content analysis of historical, historical-pedagogical, archival and reference sources on the problem under study; problem-chronological method for studying trends in the development of school education of Roma children in Transcarpathia. Results. The article deals with the historical and pedagogical retrospective of the formation and development of school education of Roma children. For the first time, the question of the need for a settled lifestyle for Roma families arose during the Austro-Hungarian Empire, when the Hungarian government issued a decree on the fulfillment of civic duty in the context of protecting the homeland on the eve of World War I and the necessity of registering Roma children, their socialization and prohibition of begging. Later, when the territory of Transcarpathia was part of the Czechoslovak Republic, the Czechoslovak government initiated the issue of involving Roma children of school age in school. In 1923, with the assistance of the Abstract of Schooling of Subcarpathian Rus in Uzhhorod, Roma children began studying in separate classes of the Uzhhorod Ukrainian School. And in 1926, twenty-two Roma children entered the first grade of the newly built Roma school, which is considered the first such educational institution in Eastern Europe. During the Soviet period, the Transcarpathian Roma maintained a secluded lifestyle, moving from one end of the country to the other in search of work, and Roma children were rarely involved in school education (mostly only in primary school). The beginning of the twenty-first century for the Transcarpathian Roma is characterized by the consolidation of actions by the regional authorities, NGOs and representatives of the Roma intelligentsia to solve the educational problems of Roma children and the gradual increase in their coverage of general secondary, vocational and higher education.

Microarray molecular mapping of horses with severe asthma.

Severe asthma (SA) in horses, resembling human asthma, is a prevalent, debilitating allergic respiratory condition marked by elevated allergen-specific immunoglobulin E (IgE) against environmental proteins; however, research exploring the exposome's influence on IgE profiles is currently limited but holds paramount significance for diagnostic and therapeutic developments. Thirty-five sports horses were analyzed, consisting of environmentally matched samples from France (5 SA; 6 control), the United States (6 SA; 6 control), and Canada (6 SEA; 6 control). This intentional cross-sectional study investigated the sensitization profiles of SA-affected and healthy horses via serological antigen microarray profiling. Partial least square-discriminant analysis (PLS-DA) was used to identify and rank the importance of allergens for class separation (ie, affected/non-affected) as variable influence of projection (VIP), and allergen with commonality internationally established via frequency analysis. PLS-DA models showed high discriminatory power in predicting SA in horses from Canada (area under the curve [AUC] 0.995) and France (AUC 0.867) but poor discriminatory power in horses from the United States (AUC 0.38). Hev b 5.0101, Cyn D, Der p 2, and Rum cr were the only shared allergens across all geographical groups. Microarray profiling can identify specific allergenic components associated with SA in horses, while mathematical modeling of this data can be used for disease classification, highlighting the variability of sensitization profiles between geographical locations and emphasizing the importance of local exposure to the prevalence of different allergens. Frequency scoring analysis can identify important variables that contribute to the classification of SA across different geographical regions.

Predicting snow damage in conifer forests using a mechanistic snow damage model and high-resolution snow accumulation data

ABSTRACT Forest damage caused by heavy wet snow accumulation in the canopy is the second most important abiotic forest disturbance agent in Nordic conifer stands after wind. The extent and frequency of snow damage in the future climate in the Nordic region is a major uncertainty. Few mechanistic models of snow damage risk to trees exist that could support forest management scenario analysis and decision making. We propose a snow damage risk model consisting of a numerical weather prediction-based snow accumulation model for forest canopies and a mechanistic critical snow load model. Snow damage probability predictions were validated on snow breakage data from the winters of 2016 and 2018 covering 3.5 million individual trees in south-eastern Norway derived from pre- and post-damage aerial laser scanning campaigns. The proposed model demonstrated satisfactory damage and no-damage class separation with an AUC of 0.72 and 0.77 in Norway spruce and Scots pine, respectively, and an F1 score of 0.7 in conifers taller than 10 m that suffered moderate stem breakage. The model achieved a classification accuracy that is comparable to that of statistical models but is simpler and requires fewer inputs.

Classification of breed combinations for slaughter pigs based on genotypes-modeling DNA samples of crossbreeds as fuzzy sets from purebred founders.

In pig production, the production animals are generally three- or four-way crossbreeds. Reliable information regarding the breed of origin of slaughtered pigs is useful, even a prerequisite, for a number of purposes, e.g., evaluating potential breed effects on carcass grading. Genetic data from slaughtered pigs can easily be extracted and used for crossbreed classification. In the current study, four classification methods, namely, random forest (RF), ADMIXTURE, partial least squares regression (PLSR), and partial least squares together with quadratic discriminant analysis (PLS-QDA) were evaluated on simulated (n = 7,500) genomic data of crossbreeds. The derivation of the theory behind PLS-QDA is a major part of the current study, whereas RF and ADMIXTURE are known and well-described in the literature. Classification success (CS) rate, square loss (SL), and Kullback-Leibler (KL) divergence loss for the simulated data were used to compare methods. Overall, PLS-QDA performed best with 99%/0.0018/0.002 (CS/SL/KL) vs. 97%/0.0084/0.051, 97%/0.0087/0.0623, and 17%/0.068/0.39 for PLSR, ADMIXTURE, and RF, respectively. PLS-QDA and ADMIXTURE, as the most relevant methods, were used on a real dataset (n = 1,013) from Norway where the two largest classes contained 532 and 192 (PLS-QDA), and 531 and 193 (ADMIXTURE) individuals, respectively. These two classes were expected to be dominating a priori. The Bayesian nature of PLS-QDA enables inclusion of desirable features such as a separate class "unknown breed combination" and informative priors for crossbreeds, making this a preferable method for the classification of breed combination in the industry.

How can a black horse have brown fur?

Hungarian equine coat colour-related terminology is a largely unresearched subject. Its unique situation as a rapidly developing jargon results in multiple nomenclatural systems used simultaneously in the specialised literature. The analysis of the nomenclatural systems’ inner structure suggests the usage of two differing methods of categorisation, one based on the visual properties of the subject, the other based on non-visual genetic factors of the described animal.
 The paper reviews the colour categories’ main traits that influence their categorisation process and compares them with the Hungarian equine coat colours’ characteristics. The comparison reveals that Hungarian equine coat colour terms are in many ways similar to the conventional colour terms, but due to their differences they create a separate colour-motivated class of specialised terms.
 The analytic part focuses on the different methods of categorisation present in the various nomenclatural systems in the equine coat colour related literature. It defines their main features and the underlying cause of their existence. It explores the division of the nomenclatural systems in accordance with their categorisation process and provides a detailed description of the traits of both traditional and modern nomenclatural systems discussing them on the basis of specific examples.
 The further part of the analysis explores the semantic relationship between the conventional colours and the equine coat colours in the context of equine coat colour terms sharing their forms with Basic Colour Terms.
 The research material consists of a database of Hungarian equine coat colour-related terms, gathered from a variety of zootechnical, sport and hobby literature and commonly available internet sources on the topic. The nomenclatural systems presented in the sources are visualised in the form of diagrams, illustrating the meaning of individual terms and the systems’ internal hierarchies.
 The presented findings showcase the complexity of the specialised colour-motivated terms and their difference from the conventional colour terms. They present how external, non-linguistic factors like the development of the natural sciences may influence the relatively hermetic colour-motivated terminology.

Computer Hardware Fault Detection based on Machine Learning

In order to solve the computer fault detection problem of machine learning, the author proposes a computer hardware fault detection problem based on machine learning. The method combines mutual information and class separability to analyze their relationship, which improves classification accuracy. This study presents an adaptive machine learning technique for the adaptive fusion of data from multiple sources. In addition, the mCRC algorithm seeks for the optimal feature subset using the enhanced forward floating search method, thereby overcoming the limitation that the mRMR algorithm does not specify how to determine the final feature subset. The classification accuracy of the mCRC algorithm is approximately 1% better than that of the mRMR algorithm, and the size of the final feature subset of the mCRC algorithm is 22% smaller than that of the final subset of the mRMR algorithm. Conclusion: the ReMAE algorithm has a higher rate of accurate failure prediction.

Exploring the Accounting Treatment of Exploration and Evaluation Activities in the Extraction Industry in South Africa and Australia

The study examines the accounting treatment of exploration and evaluation assets in the extraction industry in South Africa and Australia. This study adopted a qualitative research approach and purposive sampling techniques. Data was collected from 60 entities in the extraction industry that are listed on the Johannesburg Stock Exchange (JSE) and the Australian Securities Exchange (ASX). The study found that South African entities classify exploration and evaluation assets as tangible or intangible assets using International Financial Reporting Standard (IFRS) 6. The study also observed that Australian entities classify exploration and evaluation assets as a separate class of assets using the Australian Accounting Standard Board (AASB) 6. South African entities depreciated or amortized exploration and evaluation assets, while Australian entities did not. The study concluded that even though there is high adoption of IFRS 6 or AASB 6, comparability is compromised in the extraction industry. The study recommended that IFRS 6 not offer a choice between IAS 16 or IAS 38 for the classification of exploration and evaluation assets to improve comparability and aid effective decision-making.