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The purpose of this work is to describe the orkg-Leaderboard software designed to extract leaderboards defined as task–dataset–metric tuples automatically from large collections of empirical research papers in artificial intelligence (AI). The software can support both the main workflows of scholarly publishing, viz. as LaTeX files or as PDF files. Furthermore, the system is integrated with the open research knowledge graph (ORKG) platform, which fosters the machine-actionable publishing of scholarly findings. Thus, the systemsss output, when integrated within the ORKG’s supported Semantic Web infrastructure of representing machine-actionable ‘resources’ on the Web, enables: (1) broadly, the integration of empirical results of researchers across the world, thus enabling transparency in empirical research with the potential to also being complete contingent on the underlying data source(s) of publications; and (2) specifically, enables researchers to track the progress in AI with an overview of the state-of-the-art across the most common AI tasks and their corresponding datasets via dynamic ORKG frontend views leveraging tables and visualization charts over the machine-actionable data. Our best model achieves performances above 90% F1 on the leaderboard extraction task, thus proving orkg-Leaderboards a practically viable tool for real-world usage. Going forward, in a sense, orkg-Leaderboards transforms the leaderboard extraction task to an automated digitalization task, which has been, for a long time in the community, a crowdsourced endeavor.

Background A quantitative trait locus (QTL) is a genomic region that correlates with a phenotype. Most of the experimental information about QTL mapping studies is described in tables of scientific publications. Traditional text mining techniques aim to extract information from unstructured text rather than from tables. We present QTLTableMiner ++ (QTM), a table mining tool that extracts and semantically annotates QTL information buried in (heterogeneous) tables of plant science literature. QTM is a command line tool written in the Java programming language. This tool takes scientific articles from the Europe PMC repository as input, extracts QTL tables using keyword matching and ontology-based concept identification. The tables are further normalized using rules derived from table properties such as captions, column headers and table footers. Furthermore, table columns are classified into three categories namely column descriptors, properties and values based on column headers and data types of cell entries. Abbreviations found in the tables are expanded using the Schwartz and Hearst algorithm. Finally, the content of QTL tables is semantically enriched with domain-specific ontologies (e.g. Crop Ontology, Plant Ontology and Trait Ontology) using the Apache Solr search platform and the results are stored in a relational database and a text file. Results The performance of the QTM tool was assessed by precision and recall based on the information retrieved from two manually annotated corpora of open access articles, i.e. QTL mapping studies in tomato ( Solanum lycopersicum ) and in potato ( S. tuberosum ). In summary, QTM detected QTL statements in tomato with 74.53% precision and 92.56% recall and in potato with 82.82% precision and 98.94% recall. Conclusion QTM is a unique tool that aids in providing QTL information in machine-readable and semantically interoperable formats.

Groundwater is a vital factor affecting the stability of water-rich slope of open-pit mine, and the distribution of groundwater inside water-rich slope is always uncertain. To investigate the uncertainty of groundwater distribution inside the water-rich slope, based on the engineering background of DEZIWA open-pit mine, this paper adopts the research method of field investigation and test (single and multiple borehole pumping tests), numerical simulation (Visual MODFOW), and theoretical analysis (nonlinear fitting theory, distribution fitting theory, and one-sample Kolmogorov–Smirnov test) to study the uncertainty of the distribution of groundwater table line within the water-rich slope of open-pit mine. Results obtained from the above research indicate that, Visual MODFLOW is an effective tool for obtaining groundwater distribution information inside the water-rich slope of open-pit mine. When the open-pit mine excavated to the final boundary, it is found that within multiple cross-sections of the southern and western regions of water-rich slope of the open-pit mine, groundwater table line can all be delineated by a series of 3-term Fourier equations, which can be characterized by identical equation forms but varying fitting coefficients. Furthermore, based on the findings of distribution fitting and one-sample Kolmogorov–Smirnov test, it becomes evident that the probability distribution of the fitting coefficients of the aforementioned 3-term Fourier equations can all be described by the normal distribution models. Use the established normal distribution models in this paper, uncertainty of the groundwater distribution within the water-rich slope of DEZIWA open-pit mine can be described indirectly and quantitatively, and the research methods of this paper can provide a meaningful reference to the slope engineering with similar conditions.

The design of open-pit mines should balance safety and economy. However, safe geotechnical conditions generally involve redesigning the geometry of slopes and groundwater drawdown, significantly increasing the costs of mining operations. The use of numerical models to simulate groundwater drawdown and slope stability can be an alternative to assess cost–benefit trade-offs for decision-making. This study documents a mining plan using groundwater drawdown scenarios that illustrate how geotechnical, economic, and environmental indicators can be combined to obtain optimum slope geometry for open-pit mining. The optimization approach analyzed different scenarios of groundwater drawdown for the final pit of a phosphate mine to improve the pit slopes stability. The groundwater simulation scenarios included the combination of deep horizontal drains and pumping wells. Stability analyses using the limit equilibrium method were used to obtain the bench, inter-ramp, and overall factors of safety of different representative sections. The factors of safety obtained, the drawdown costs and the water table elevation of each section were selected as indicators for obtaining the optimal drawdown scenario using a multi-objective tool. The groundwater control system obtained with 11 horizontal drains and 1 pumping well was considered the most adequate from the geotechnical and economic perspectives. Slope geometry optimization obtained with this drawdown scenario led to adequate inter-ramp and overall safety factors for the final pit design, reducing the barren-to-ore ratio to 0.38, much less than the present ratio (≈ 3). The results are important for optimizing the slope geometry of open-pit mines and can be replicated in other regions.

A finite-element groundwater flow model was developed for the expanding Dareh-Zar open pit mine in southern Iran, to simulate groundwater inflow into the excavation and mine wall pore pressure dynamics. The model was used to test the effectiveness of implementing different drainage management strategies to reduce groundwater inflow rates and mine wall pore pressures, including abstraction wells and horizontal drains. Model predictions suggest the implementation of abstraction wells will reduce groundwater inflow rates by 75% during the first 12 years of mining and 50% during the subsequent 5 years relative to a ‘no drainage’ management scenario, with further reductions in groundwater inflow achieved through horizontal drain installation. Furthermore, the installation of horizontal drains was found to be necessary to reduce mine-wall pore pressures from destabilizing the mine walls. Groundwater management of the decommissioned pit mine was also evaluated, with simulation results suggesting that backfilling the excavation would restore the groundwater level within the open pit mine region to ~2,442 m above sea level, representing a net restoration of ~204 m relative to the water table prior to mine closure.

Open-pit mining has increased substantially over the past two decades. Many currently operating open-pit mines are facing the end of mine-life over the next few decades and, increasingly, focus is shifting towards mine-closure planning that provides evidence on available closure options under the given geological, hydro(geo)logical and climatic conditions. This study uses synthetic groundwater modelling to build basic process understanding of closure options and how these will determine the formation of pit lakes. This governs the long-term pit lake water quality and how postmining landscapes may be utilised. Simulations show that the recovery time of postmining groundwater levels increases with decreasing aquifer transmissivity. Final postmining water tables are predominantly controlled by the implemented mine closure options and climatic conditions. The most important decision is, thereby, whether to backfill the pit to above the water table or allow a pit lake to develop. Under moderately transmissive aquifer settings, backfilling of pits leads to rapidly rising groundwater levels within the first decade after mining, with water-table recoveries of above 70%. If mine voids remain unfilled, evaporation from the pit lake surface becomes a governing factor in determining whether the unfilled mine pit becomes a terminal sink for groundwater. Lake levels may remain subdued by several 10s of metres in arid to semiarid climates. If surplus surface water can be diverted into open pits, rapid filling can accelerate groundwater recovery of open pits in regions of low permeability. This is a less successful management option in transmissive aquifers.

Mining activity introduces severe changes in landscapes and, subsequently, in land uses. One of the most singular changes is the existence of pit lakes, which occur in active and, more frequently, abandoned mines. Pit lakes are produced by water table interception when open-pit mines deepen. Their characteristics are highly variable, depending on the type of mine, the environment or the climate. In León province there is a long tradition of coal mining that dates back to the nineteenth century, and hundreds of open pits from the 1970s to 2018 have been opened, producing permanent landscape changes. This work analyses the main parameters, including morphological measurements, depth and pH values obtained from aerial photos and field work, of 76 coal pit lakes more than 30 m in length. The vast majority of these pit lakes were unknown until now and were not included in inventories or maps. The data obtained provide baseline knowledge that will allow, in the future, potential uses (storage of water for various uses, recreational use, wildlife habitat, and geological heritage sites) for these pit lakes and establish their importance as a new geoecological environment.

Underground coal mining inevitably causes land subsidence, while negatively impacting land and ecological environments. This is particularly severe in coal-grain overlap areas (CGOA) in eastern China, which have high groundwater levels. Mining subsidence has substantially altered the original topography, and raised the groundwater level, which threatens grain security in the region. Therefore, it is necessary to determine the damaged farmland area in the CGOA. The traditional method to define the range of coal mining disturbance is usually based on surface subsidence. However, this fails to consider the multidimensional impacts of coal mining on the ecology, which is considered unreasonable. Therefore, this paper introduces a low-cost, fast, and non-destructive method for land damage assessment in a typical CGOA in eastern China, using maize aboveground biomass (AGB) as estimated from an unmanned aerial vehicle (UAV). There were three key results from the survey. (1) underground coal mining caused significant ecological problems in the study area, including subsidence (approximately 6 m) and the degradation of vegetation (maize AGB in a range of 192.73–1338.06 g/m 2 ). In addition, the degradation of maize was affected by subsidence (0.61 ** Pearson coefficient found between the AGB and surface elevation). (2) An UAV combined with multispectral and digital cameras, allowed precise estimation of the AGB and the red-edge chlorophyII index (CI rededge ) combined with the elevation factor had the best explanatory power using the random forest (RF) method ( R 2  = 0.96, RMSE = 65.03 g/cm 2 ). (3) The maize AGB could be used to assess land damage affected by underground coal mining, which accounted for 82.12% of the study area. The results of the study could provide a reference for land damage assessments in the CGOA, while also providing a guide for land reclamation and agricultural management decisions in the region.

Anthropogenic activities have great impacts on ecological environmental protection. Landscape patterns are important indicators of ecological risks. Therefore, effectively and accurately predicting landscape pattern variations is vital for qualitatively identifying the ecological risks faced by an area, especially in ecologically fragile underground coal mining areas with intense anthropogenic mining activities. Conventional landscape pattern prediction models mainly consider the driving factors of climatic and topographical conditions, whereas geological conditions are rarely considered. To overcome this limitation, seven representative driving factors of landscape pattern variations including two geological property-related factors (the thickness of coal seams and groundwater level), two climatic factors (precipitation and evaporation), and three topographical factors (elevation, slope, and NDVI which is abbreviated for the normalized difference vegetation index) were selected for statistical correlation with six major landscape pattern indices. Landscape pattern variation prediction models were subsequently developed using three different machine learning approaches and applied in the Yushenfu mining area of China. The model validation results showed that the model using the particle swarm optimization–extreme learning machine (PSO–ELM) method outperformed that using an extreme learning machine (ELM) and an artificial neural network (ANN). Furthermore, the spatial distribution law of the six landscape pattern indices under coal mining conditions was predicted using the PSO–ELM-based prediction model. The results show that the average increase rates of landscape pattern indices LSI, DP, LPI, and PSA are 7.6%, 26.1%, 44.9%, and 135.8%, respectively; whereas, the average decrease rates of LDI and SHDI are 19.1% and 12.5%, respectively, after mining in the #3 and #4 planning areas. Mining activities reduced the diversity of landscape patterns and increased regional ecological risks, as two high-risk areas were identified. The proposed prediction models were proven to be useful for planning mining areas and protecting local ecological environments.

Shaft stability evaluation (SSE) is one of the most crucial and important tasks in view of the role of vertical shaft in mining engineering, the accuracy of which determines the safety of on-site workers and the production rate of target mine largely. Existing artificial methods are limited to the amount of data and complex process of modeling as well as rare consideration of comprehensive evaluation model in this field. In this way, this paper introduces a high-efficient model that incorporating the unascertained measurement (UM) and multiple weights (the analysis hierarchy process, entropy and the criteria importance through intercriteria correlation) to meet the engineering requirements. Simultaneously, the main parameters, including surface subsidence velocity, cumulative surface subsidence(CSS), loose strata thickness(LST), the water level drop in aquifer (WLD), shaft wall thickness, construction methods and shaft wall types, and diameter ratio of shaft and shaft lining quality, are prepared to analyze the shaft stability. Linear and nonlinear membership functions are utilized to investigate the index correlation belonging to different risk levels. The stability class is determined through the index measurement vectors and classic classification criteria considering the individual index importance. The confusion matrix-based results show that the ensemble model with optimal structure has inspired performance in SSE with 100% accuracy. Furthermore, the shaft is sensitive to the factors CSS, LST and WLD using the sensitivity analysis. Additionally, some parameters associated with the shaft stability are investigated from Daye Iron mine (China) to validate the applicability of target model, the results of which are consistent to the on-site conditions perfectly. Findings reveal that the constructed model has great potential in assessing the shaft stability, which is beneficial to eliminate the risk of shaft failure in time.