Sampling Depth Research Articles

Nutrient transformations based on sampling scheme and cropping system following subsurface‐banded poultry litter

AbstractPoultry litter (PL) is an excellent source of micro‐ and macronutrients. However, surface applications result in greater nutrient runoff and nitrogen loss via ammonia volatilization. Subsurface banding PL is a promising technology for combating these challenges, but scant information exists on proper soil sampling techniques and management recommendations for subsurface‐applied PL. Therefore, objectives were to quantify the nutrient status based on sampling depth (0–15 cm and 15–30 cm) and schema (systematic [0, 3, 7, and 10 cm from PL bands] and composite) to develop subsurface PL recommendations per system (annual cropping and perennial pasture). Soil samples were collected during PL application (subsurface and surface) and 1, 6, 12, 18, and 24 months thereafter. On average, total N, Mehlich‐3 extractable P, and Mehlich‐3 extractable K were 15%, 96%, and 72% greater, respectively, for subsurface compared to surface applications. Further, Mehlich‐3 and water‐soluble P at the 0‐ to 15‐cm depth were 4–5 and 2–3 times greater in soils receiving subsurface PL in perennial pasture and row crop systems, respectively, compared to surface applications, likely owing to lesser nutrient losses to the air, soil, and water under subsurface PL systems. Compared to surface applications, subsurface PL increased (p < 0.05) N, P, and K crop removal by 75%, 70%, and 72%, respectively, and resulted in 80% and 78% yield increases and N‐use efficiency, respectively. Consequently, subsurface PL conserved greater N, P, and K at the 0‐ to 15‐cm depth, thus increasing nutrient‐use efficiency in row crop systems and improving water quality in sensitive watersheds.

Turnover importance: Operationalizing beta diversity to quantify the generalism continuum

Abstract Generalization is difficult to quantify, and many classifications exist. A beta diversity framework can be used to establish a numeric measure of generalist tendencies that jointly describes many important features of species interactions, namely spatiotemporal heterogeneity. This framework is promising for studying generalized symbiotic relationships of any form. We formulated a novel index, turnover importance (T). T describes spatiotemporal heterogeneity in interactor assemblages, an inherent feature of generalist relationships that is not captured by available metrics. We simulated the behaviour of T relative to other available metrics, calculated T for native North American orchid‐insect relationships, and tested correlations between T and eco‐geographic variables. We performed case studies to demonstrate applications of T for conservation and eco‐evolutionary studies. T behaves predictably across simulations, and dynamically interacts with site number, gamma diversity, and species range sizes. T is moderately sensitive to sampling depth. Orchids with higher T scores occupy larger ranges and broader climatic niches. Alternative interactor‐specific measures of generalism are best employed for local‐level community networks over short timespans. While these interactor metrics can assess use versus availability in local communities, T can be used to measure spatiotemporal patterns of variation in interactor assemblages across a focal species' range. This study provides a roadmap for future work focused on better understanding the patterns and consequences of generalized relationships.

A dataset for soil organic carbon in agricultural systems for the Southeast Asia region

The determination of changes in soil organic carbon (SOC) content under different cropping systems is necessary for policy development oriented towards soil conservation, C sequestration, and future C credit markets. The aim of this study was to generate an open SOC dataset resulting from a systematic literature search related to the agricultural systems for Southeast Asia. The dataset has 209 articles and 4341 observations on soils of cropping systems in this region from articles published between 1987 and 2023. This dataset included different management practices, land uses, soil sampling depth, and length of SOC content assessment. In addition, inherent features of crop production reported in the experiments were included in the dataset. This dataset can be applied to quantify and compare the impact of different land uses or management practices on SOC content, providing foundational knowledge towards identifying sustainable practices. Lastly, it is a useful guide for future regional SOC sequestration policies and the development of C credit markets.

Effects of Sampling Time and Depth on Phytoplankton Metrics in Agricultural Irrigation Ponds

Spatiotemporal variations of phytoplankton populations in agricultural irrigation ponds need to be accounted for in order to properly assess water quality. Phytoplankton cell and photosynthetic pigment concentrations are two common metrics used to characterize phytoplankton communities. This work evaluated depth and time of the day as factors affecting discrete sampling of phytoplankton. The abundance of chlorophytes, diatoms, cyanobacteria, flagellates, and dinoflagellates, as well as chlorophyll-a and phycocyanin pigments, were determined in samples taken at the surface and depth, in 0.5m increments, in three to five spatial replications at 9 am, 12 pm, and 3 pm in two ponds in Maryland, USA. Depth was a significant factor for photosynthetic pigment concentration variations in both ponds on most sampling dates and time of day was a significant factor for photosynthetic pigment concentrations in half of the sampling dates. Depth was not a significant factor in cell concentration variations for any of the phytoplankton groups observed, but time of day was a significant factor in 40% of the sampling dates. Two distinct patterns in pigment concentration daily variation were observed. The first featured a continuous increase with depth throughout the day. The second showed maximum concentrations at the surface in the morning changing to maximum concentrations at 0.5m depth at 12 pm and 3 pm; these patterns corresponded to different morning solar irradiance levels. This indicates that sampling depth and time can be a significant factor when evaluating photosynthetic pigments and should be accounted for in monitoring programs that rely on pigments for decision-making.

An investigation of the source of calcium carbonate from the carbonated Portland cement: Based on the transformation of hydrates

In this work, two PC pastes were used to carry out accelerated carbonation experiments, and phase assemblages of paste samples collected at different sampling depths were analyzed. The source of CaCO3 originating from the carbonation reaction of hydrates in cement was quantitatively determined by a novel method based on the transformation of hydrates. Experimental results indicated that the phase assemblages in the carbonated zone of two PC pastes showed minor differences. C(A)SH, ettringite, AFm-CO3, and unhydrated cement particles in the complete carbonation zone of two cement pastes were carbonated and transformed into other phases. For the surface of two PC pastes (0–3 mm), calculations showed that the CaCO3 originating from the carbonation of calcium hydroxide and C(A)SH accounted for 75.3% and 72.6% of the total CaCO3, respectively. A concentration of 20% CO2 was not beneficial for the diffusion of CO2 and the subsequent precipitation of CaCO3 at the later period of carbonation.

Assessing nitrate groundwater hotspots in Europe reveals an inadequate designation of Nitrate Vulnerable Zones

Monitoring networks show that the European Union Nitrates Directive (ND) has had mixed success in reducing nitrate concentrations in groundwater. By combining machine learning and monitored nitrate concentrations (1992–2019), we estimate the total area of nitrate hotspots in Europe to be 401,000 km2, with 47% occurring outside of Nitrate Vulnerable Zones (NVZs). We also found contrasting increasing or decreasing trends, varying per country and time periods. We estimate that only 5% of the 122,000 km2 of hotspots in 2019 will meet nitrate quality standards by 2040 and that these may be offset by the appearance of new hotspots. Our results reveal that the effectiveness of the ND is limited by both time-lags between the implementation of good practices and pollution reduction and an inadequate designation of NVZs. Substantial improvements in the designation and regulation of NVZs are necessary, as well as in the quality of monitoring stations in terms of spatial density and information available concerning sampling depth, if the objectives of EU legislation to protect groundwater are to be achieved.

Littoral phytoplankton and zooplankton communities in a tropical reservoir: Pantabangan Reservoir, the Philippines

Abstract Littoral phytoplankton and zooplankton diversity are influenced by the water quality parameters and their trophic interactions. In the largest reservoir of the Philippines, this study examined the water quality and plankton community composition in the littoral zone across three sampling sites and four depths. Of the three sites, one is located near active fish cages, one near the mouth of a tributary and one near an outlet of an upstream dam. A total of 36 phytoplankton and 34 zooplankton taxa were identified, with most taxa belonging to Chlorophyta and Cyclopoida. The most abundant phytoplankton and zooplankton taxa were Aulacoseira and Microcyclops, respectively. The three most abundant phytoplankton (Aulacoseira, Oscillatoria, Volvox) were most positively correlated with the nutrient levels [total nitrogen (TN), total phosphorus (TP) and TN:TP ratio]. A significant interaction of sampling site and depth was recorded for the phytoplankton’s Shannon diversity index and community composition. The decreased zooplankton diversity amidst decreased phytoplankton diversity in the aquaculture site aligns with the effect of lowered resource heterogeneity on zooplankton. The increased phytoplankton diversity and decreased zooplankton density and taxon richness at shallower depths were probably linked with the light availability.

Accumulation characteristics and ecological risk evaluation of microplastics in sediment cores from the artificial reef area and surrounding seas of Haizhou Bay, north China

One significant “sink” for microplastic (MP) pollution is the sediments. There's a considerable lack of reliable data regarding the historical status of MPs contamination in sediments within marine ranching. In this research, the study area encompassed Haizhou bay marine ranching and adjacent seas. The primary objective was to explore the potential relationships between the accumulation of MPs and both the sample depth and sediment characteristics within the cores. The results unveiled significant contamination of MPs within the sediment cores. The average MPs concentration of sediment was 1.01 ± 1.28 n/g. Fibrous polymers and particles smaller than 1000 μm were frequently found in the sediment. The abundance of MPs exhibited a tendency to decrease with an increase in sediment depth. Artificial reefs and currents affected on MPs distribution in sediment cores. The accumulation of MPs showed a significant correlation (P < 0.05) with the sediment content of different particle sizes, suggesting that the composition of sediment can serve as an indicator of the abundance of MPs. The risk of MP pollution in the sediments of the study area was assessed by establishing a risk assessment model using concentration data of MPs and polymer types. Due to the higher hazard score of polymers (PA and PET) in MPs, the Polymer hazard index (PHI) was elevated to grade II. However, it had a Pollution load index (PLIzone) value of 1.95 (level I). This suggested that contamination was minimal, yet the ecological risk remained relatively high. The ecological risk assessment of MPs served as the foundation for gaining a detailed understanding of the distribution characteristics of MPs. It also furnished essential data support for conducting a comprehensive assessment, developing feasible management strategies, and establishing water quality standards related to plastic waste.

Differences in diversity and community composition of the shell microbiome of apparently healthy lobsters Homarus americanus across Atlantic Canada.

Host-microbe dynamics are of increasing interest in marine research due to their role in host health and productivity. Changes in the shell microbiome of American lobsters have been associated with epizootic shell disease, a syndrome that is spreading northwards across the eastern U.S. and Canadian Atlantic coast. This study analyzed differences in alpha and beta diversity, as well as differentially abundant taxa, in the shell-associated bacterial community of apparently healthy lobsters from four lobster fishing areas (LFAs) in Atlantic Canada. Over 180 lobsters from New Brunswick, Nova Scotia and Prince Edward Island (PEI) were sampled during seven sampling events over four sampling months. The bacterial community was identified using novel PacBio long-read sequencing, while alpha and beta diversity parameters were analyzed using linear regression models and weighted UniFrac distances. The bacterial richness, diversity and evenness differed by sampling location, sampling month, and molt stage, but not by lobster sex or size, nor sampling depth. Similarly, based on LFA, sampling month, year and lobster molt stage, the shell microbiome differed in microbial community composition with up to 34 out of 162 taxa differing significantly in abundance between sampling groups. This large-scale microbial survey suggests that the shell microbial diversity of apparently healthy lobsters is influenced by spatial and temporal factors such as geographic location, as well as the length of time the carapace is exposed to the surrounding seawater.

Soil carbon dynamics in drained prairie pothole wetlands

Drainage leads to trade-offs between crop production efficiency and wetland conservation, with complex impacts on ecosystem services. In North America’s Prairie Pothole Region, wetland drainage is widespread, often to increase the available land for cultivation, prevent crop loss due to flooding, and manage soil salinity. Wetlands are known for providing key ecosystem services such as improved water quality, flood mitigation, and carbon storage. There is limited research on how changes to soil hydrology and soil redistribution through wetland drainage can impact soil carbon storage and persistence in this region. This research evaluates factors that contribute to soil carbon storage in drained prairie pothole wetland based on 33 drained wetlands in Saskatchewan, Canada. These analyses showed regional differences in the response of soil carbon storage to drainage that are driven by environmental factors such as annual precipitation, temperature, and wetland permanence. We observed increasing soil carbon storage from the Dark Brown to Black soil zones, as well as with longer wetland pond permanence. The sampling depth used for calculating soil carbon storage was especially important when comparing geographically across the soil zones as the Black soil zone had greater soil carbon stored at depth. Soil carbon was also intensively monitored over 2 years following installation of surface drainage across a wetland complex (8 drained wetlands) where water was partially directed to a consolidation wetland. We further assessed changes in soil carbon dynamics and protection from microbial decomposition based on three soil organic matter fractions, ATR-FTIR for organic matter functional groups, and phospholipid fatty acid analysis to understand the microbial community abundance and structure. After 2 years following drainage, ephemeral wetlands with short pond permanence were found to be most sensitive to drainage and the only wetland class with decreases in soil carbon. The temporary and seasonal wetland classes showed no significant differences in soil carbon content but there were changes in the organic matter with depth due to soil redistribution during drainage implementation. Jointly, this research provides region-specific estimates of soil carbon storage in drained prairie pothole wetlands that can be used to inform wetland soil carbon management in cultivated fields.

Soil bacterial community in a photovoltaic system adopted different survival strategies to cope with small-scale light stress under different vegetation restoration modes.

Solar photovoltaic (PV) power generation is a major carbon reduction technology that is rapidly developing worldwide. However, the impact of PV plant construction on subsurface microecosystems is currently understudied. We conducted a systematic investigation into the effects of small-scale light stress caused by shading of PV panels and sampling depth on the composition, diversity, survival strategy, and key driving factors of soil bacterial communities (SBCs) under two vegetation restoration modes, i.e., Euryops pectinatus (EP) and Loropetalum chinense var. rubrum (LC). The study revealed that light stress had a greater impact on rare species with relative abundances below 0.01% than on high-abundance species, regardless of the vegetation restoration pattern. Additionally, PV shadowing increased SBCs' biomass by 20-30% but had varying negative effects on the numbers of Operational Taxonomic Unit (OTU), Shannon diversity, abundance-based coverage estimator (ACE), and Chao1 richness index. Co-occurrence and correlation network analysis revealed that symbiotic relationships dominated the key SBCs in the LC sample plots, with Chloroflexi and Actinobacteriota being the most ecologically important. In contrast, competitive relationships were significantly increased in the EP sample plots, with Actinobacteriota having the most ecological importance. In the EP sample plot, SBCs were found to be more tightly linked and had more stable ecological networks. This suggests that EP is more conducive to the stability and health of underground ecosystems in vulnerable areas when compared with LC. These findings offer new insights into the effects of small-scale light stress on subsurface microorganisms under different vegetation restoration patterns. Moreover, they may provide a reference for optimizing ecological restoration patterns in fragile areas.

Insights into the pelagic ciliate community in the Bering Sea: Carbon stock, driving factors and indicator function for climate change

Microzooplankton carbon stock and its biotic-abiotic interactions are important linkage in marine carbon biogeochemistry, which remain poorly documented in the Bering Sea. In the summer of 2019, we investigated the community structure of pelagic ciliates from the surface to bottom (or up to 200 m) in the Bering Sea. High abundance and biomass of ciliate occurred mainly in waters shallower than 50 m. The average carbon stock of ciliates were 350.0 ± 96.4 mg C m−2 and 218.4 ± 148.8 mg C m−2 in oceanic and shelf regimes, respectively. Aloricate ciliates were the dominant group, with an average abundance proportion to total ciliates of over 80% at each depth. We identified 45 tintinnid species belonging to 15 genera, including 7 new record species in the Bering Sea. The abundance proportion of their lorica oral diameter (LOD) size-classes at each sampling depth was mainly within the fractions of 12–16 μm and 24–28 μm. Furthermore, both Codonellopsis frigida and Parafavella jorgenseni can act indicator species of Pacific inflows based on their distributions. Strong correlations were reflected between abiotic environmental factors and the distribution of these indicator species, for instance, the positive correlation between salinity and the cold water-adapted species C. frigida, confirming the dominant role of bottom-control on the ecosystem. Our study laid a foundation for deep understanding of ciliate community at summertime of the Bering Sea, and its role in driving biogeochemical fluxes.

Machine Learning Method to Explore the Correlation between Fly Ash Content and Chloride Resistance.

Chloride ion corrosion has been considered to be one of the main reasons for durability deterioration of reinforced concrete structures in marine or chlorine-containing deicing salt environments. This paper studies the relationship between the amount of fly ash and the durability of concrete, especially the resistance to chloride ion erosion. The heat trend map of total chloride ion factor correlation displayed that the ranking of factor correlations was as follows: sampling depth > cement dosage > fly ash dosage. In order to verify the effect of fly ash dosage on chloride ion resistance, three different machine learning algorithms (RF, GBR, DT) are employed to predict the total chloride content of fly ash proportioned concrete with varying admixture ratios, which are evaluated based on R2, MSE, RMSE, and MAE. The results predicted by the RF model show that the threshold of fly ash admixture in chlorinated salt environments is 30-40%. Replacing part of cement with fly ash in the mixture of concrete below this threshold of fly ash, it could change the phase structure and pore structure, which could improve the permeability of fly ash concrete and reduce the content of free chloride ions in the system. Machine learning modeling using sample data can accurately predict concrete properties, which effectively reduce engineering tests. The development of machine learning models is essential for the decarbonization and intelligence of engineering.

Assessing transmission attribution risk from simulated sequencing data in HIV molecular epidemiology.

HIV molecular epidemiology (ME) is the analysis of sequence data together with individual-level clinical, demographic, and behavioral data to understand HIV epidemiology. The use of ME has raised concerns regarding identification of the putative source in direct transmission events. This could result in harm ranging from stigma to criminal prosecution in some jurisdictions. Here we assessed the risks of ME using simulated HIV genetic sequencing data. We simulated social networks of men-who-have-sex-with-men, calibrating the simulations to data from San Diego. We used these networks to simulate consensus and next-generation sequence (NGS) data to evaluate the risks of identifying direct transmissions using different HIV sequence lengths, and population sampling depths. To identify the source of transmissions, we calculated infector probability and used phyloscanner software for the analysis of consensus and NGS data, respectively. Consensus sequence analyses showed that the risk of correctly inferring the source (direct transmission) within identified transmission pairs was very small and independent of sampling depth. Alternatively, NGS analyses showed that identification of the source of a transmission was very accurate, but only for 6.5% of inferred pairs. False positive transmissions were also observed, where one or more unobserved intermediaries were present when compared to the true network. Source attribution using consensus sequences rarely infers direct transmission pairs with high confidence but is still useful for population studies. In contrast, source attribution using NGS data was much more accurate in identifying direct transmission pairs, but for only a small percentage of transmission pairs analyzed.

Diversity and abundance of archaeal amoA genes in the permanent and temporary oxygen minimum zones of Indian Ocean

Oxygen minimum zones are results of oxygen consumption exceeding the oxygen availability in stratified water columns of the marine environment. We compared the ammonia monooxygenase subunit A (amoA) gene abundance and the diversity of ammonia-oxidising archaea (AOA) in the Arabian Sea (AS) with those of the Bay of Bengal (BoB). Three primer pairs targeting amoA genes of water column A (WCA), water column B (WCB) and total AOA (amoAt) captured different densities of gene copy numbers in both marginal seas. Water column A (WCA) ecotypes were more abundant in the AS than in the BoB. Core-OMZ depths of the BoB contained 10 times lower amoA copy numbers than those of the AS. Along with sampling depth, concentration of ammonia shapes the WCA/WCB ecotypes in AS/BoB. Among the total AOA populations, WCB ecotypes were more abundant. The amoA gene sequences were either of Nitrosopumilales or Ca. Nitrosotaleales members and belonged to NP-γ, NP-δ, NS-β, NS-γ and NS-ε sub-clades. Pairwise distance and nucleotide diversity index analysis reveals that BoB nurtures two times more diverse amoA sequences than the AS. The core OMZ region of the BoB contains a two-fold higher diversity of amoA gene sequences compared to the AS, whereas the AS contains 13 times more abundant amoA copies than the BoB.

Depth distribution of plant-parasitic nematodes on bentgrass golf greens in Missouri and Indiana.

Control of plant-parasitic nematodes (PPNs) on golf putting greens with nematicides is dependent on the seasonal occurrence and depth distribution of target PPN populations. This study aimed to determine if plant-parasitic nematode populations on golf course putting greens in Missouri and Indiana peaked at a targetable depth at a specific time in the year, focusing primarily on lance (Hoplolaimus spp.) and root-knot (Meloidogyne spp.) nematodes. To elucidate species diversity in the region, rDNA from a subset of lance and root-knot nematodes was sequenced and analyzed, with additional micromorphology of a lance nematode assessed in scanning electron micrographs (SEM). Soil samples were taken to a depth of 25 cm and stratified into 5 cm increments during April, June, August and October at seven sites across Missouri, three in the Kansas City metro of Kansas in 2021 and in ten sites across Indiana in 2022. Samples were stratified in five-centimeter increments and aggregated for a total of 100 cm3 of soil at each depth for each sampling. Samples were processed using a semi-automatic elutriator followed by the sucrose-flotation method, and populations were counted using a hemocytometer and recorded. For molecular characterization, rDNA was extracted and analyzed from 31 individual lance nematodes from one site in Missouri and eight sites in Indiana, and 13 root-knot nematodes from nine sites across Indiana. A significant interaction occurred between sampling month and depth for lance and ring nematodes Missouri/KS, with both PPN populations peaking at the 0-5 cm depth during October, which is well after most targeted nematicide applications are applied. Ring nematodes in Indiana did not follow this trend and were most abundant in August at a depth of 0-5 cm. No significant interaction between depth and month occurred for lance or root-knot nematodes in Indiana, or root-knot nematodes in Missouri/KS. Hoplolaimus stephanus and H. magnistylus were the lance species identified on golf greens, and Meloidogyne naasi, M. graminicola and M. marylandi were the root-knot species identified. Scanning-electron micrographs confirmed morphological characteristics unique to H. stephanus.

Study on the method of evaluating the failure of stator bar insulation materials based on mechanical damage characteristics

The damage impact of mechanical shocks on stator bar insulation is a key factor in high-voltage motor accidents. This study conducted electrical aging acceleration and breakdown experiments on damaged stator insulating materials to understand failure causes and evaluation methods. The relationship between sample defect area and depth and electrical properties was analyzed, along with the morphology of the damaged surface using a scanning electron microscope. Results show that the defect area correlates with impact mode but does not directly impact electrical performance. Effective insulation thickness is positively linked to electrical performance. This study serves as a valuable reference for evaluating mechanical impact faults in high-voltage motors.

A three-dimensional sampling design based on the coefficient of variation method for soil environmental damage investigation.

A traditional grid model for soil sampling may suffer from poor efficiency and low accuracy. With a nonferrous metal processing plant as the study area, a three-dimensional kriging interpolation model was built based on this plant's preliminary investigation data for arsenic (As), and a detailed survey sampling programme was proposed. The sampling density at the pollution interval of the surface soil was estimated by the coefficient of variation method, and the sampling depth was determined by the pollution interval of the vertical prediction results. The results showed that the encrypted soil sampling distribution optimisation method obtains greater pointing accuracy with fewer points. The sampling accuracy was 87.62% after optimising the depth of pointing. Moreover, this approach could save 66.13% of the sampling costs and 56.93% of the testing costs compared to a full deployment programme. This study provides a new and cost-effective method for predicting the extent of contamination exceedance at a site and provides valuable information to guide post-remediation strategies for contaminated sites.

Deep Learning-Based Medical Image Registration Algorithm: Enhancing Accuracy with Dense Connections and Channel Attention Mechanisms

In critical clinical medical image analysis applications, such as surgical navigation and tumor monitoring, image registration is crucial. Recognizing the potential for enhanced accuracy in existing unsupervised image registration techniques for single-modal imagery, this research introduces an innovative deep learning-based image registration algorithm. Its novelty resides in integrating short and long connections to create a densely connected structure, markedly refining the feature map interconnectivity within the U-Net architecture. This advancement addresses the significant semantic gap issues arising from disparities in feature map sampling depths. Moreover, the algorithm incorporates a channel attention mechanism within the U-shaped network's decoder, significantly mitigating image noise and facilitating the generation of smoother deformation fields. This enhancement not only boosts the model's detail sensitivity but also markedly increases image registration precision, particularly evident when processing single-modal brain MRI datasets, thereby proving the algorithm's efficacy and utility. Extensive clinical application-based training and testing have underscored this algorithm's substantial contributions to medical image registration accuracy enhancement. Overall, by leveraging deep learning technologies and innovative algorithmic structures, this study addresses pivotal challenges in medical image registration, offering more precise and dependable support for clinical applications like surgical navigation and tumor surveillance.

Breast cancer classification based on hybrid machine learning model

This study proposes a hybrid model that combines K-Means clustering and Random Forest classification as an approach for breast cancer classification. The objective is to exploit the advantages of unsupervised clustering and supervised classification techniques to enhance the accuracy and robustness of classification models. The dataset underwent preprocessing procedures encompassing the handling of missing values, feature normalization, and feature selection. Missing values were addressed through appropriate methods, and features were scaled and selected based on variance threshold or correlation analysis. Subsequently, K-Means clustering was applied to the preprocessed data to assign cluster labels to each sample. The study then proceeded to train a Random Forest classifier by incorporating both the cluster labels and the raw gene eigenvalues as mixed features. This integration of gene expression values and cluster labels provides supplementary information to the classifier, enabling the capture of more intricate patterns within the data. The Random Forest classifier was trained using optimized parameters determined through parameter tuning, including the number of trees, maximum depth, and minimum number of split samples. Extensive experiments and evaluations conducted in this study revealed that the hybrid model outperformed the standalone Random Forest classification. The incorporation of K-Means clustering facilitated the discovery of underlying data structures and patterns, ultimately enhancing the classifier's discriminatory ability. The hybrid model exhibited superior accuracy, precision, recall, and F1 scores, demonstrating its efficacy in accurately classifying breast cancer samples.