58 citations as recorded by crossref.

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2. Quantitative Expression of the Burial Phenomenon of Deep Seafloor Manganese Nodules A. Tsune 10.3390/min11020227
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4. Using Habitat Classification to Assess Representativity of a Protected Area Network in a Large, Data-Poor Area Targeted for Deep-Sea Mining K. McQuaid et al. 10.3389/fmars.2020.558860
5. Estimation Accuracy and Classification of Polymetallic Nodule Resources Based on Classical Sampling Supported by Seafloor Photography (Pacific Ocean, Clarion-Clipperton Fracture Zone, IOM Area) J. Mucha & M. Wasilewska-Błaszczyk 10.3390/min10030263
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9. Environmental Heterogeneity Throughout the Clarion-Clipperton Zone and the Potential Representativity of the APEI Network T. Washburn et al. 10.3389/fmars.2021.661685
10. Using Robotics to Achieve Ocean Sustainability During the Exploration Phase of Deep Seabed Mining N. Agarwala 10.4031/MTSJ.57.1.15
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13. Ophiotholia (Echinodermata: Ophiuroidea): A little-known deep-sea genus present in polymetallic nodule fields with the description of a new species A. Eichsteller et al. 10.3389/fmars.2023.1056282
14. Optical Imaging and Image Restoration Techniques for Deep Ocean Mapping: A Comprehensive Survey Y. Song et al. 10.1007/s41064-022-00206-y
15. Tidally Driven Dispersion of a Deep-Sea Sediment Plume Originating from Seafloor Disturbance in the DISCOL Area (SE-Pacific Ocean) M. Baeye et al. 10.3390/geosciences12010008
16. Monitoring of Anthropogenic Sediment Plumes in the Clarion-Clipperton Zone, NE Equatorial Pacific Ocean S. Haalboom et al. 10.3389/fmars.2022.882155
17. Linkages between sediment thickness, geomorphology and Mn nodule occurrence: New evidence from AUV geophysical mapping in the Clarion-Clipperton Zone E. Alevizos et al. 10.1016/j.dsr.2021.103645
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19. Distribution of free-living marine nematodes in the Clarion–Clipperton Zone: implications for future deep-sea mining scenarios F. Hauquier et al. 10.5194/bg-16-3475-2019
20. Extensive Coverage of Marine Mineral Concretions Revealed in Shallow Shelf Sea Areas L. Kaikkonen et al. 10.3389/fmars.2019.00541
21. Global Observing Needs in the Deep Ocean L. Levin et al. 10.3389/fmars.2019.00241
22. Monitoring of a sediment plume produced by a deep-sea mining test in shallow water, Málaga Bight, Alboran Sea (southwestern Mediterranean Sea) S. Haalboom et al. 10.1016/j.margeo.2022.106971
23. The contribution of microbial communities in polymetallic nodules to the diversity of the deep-sea microbiome of the Peru Basin (4130–4198 m depth) M. Molari et al. 10.5194/bg-17-3203-2020
24. An Integrative Taxonomic Survey of Benthic Foraminiferal Species (Protista, Rhizaria) from the Eastern Clarion-Clipperton Zone O. Himmighofen et al. 10.3390/jmse11112038
25. An acquisition, curation and management workflow for sustainable, terabyte-scale marine image analysis T. Schoening et al. 10.1038/sdata.2018.181
26. CFD-DEM simulation and experimental investigations on continuous hydraulic sampling of deep-sea polymetallic nodules Y. Dai et al. 10.1016/j.oceaneng.2024.119509
27. Deep-Sea Mining—A Bibliometric Analysis of Research Focus, Publishing Structures, International and Inter-Institutional Cooperation R. Kleiv & M. Thornhill 10.3390/min12111383
28. Ecology of a polymetallic nodule occurrence gradient: Implications for deep‐sea mining E. Simon‐Lledó et al. 10.1002/lno.11157
29. Megafaunal variation in the abyssal landscape of the Clarion Clipperton Zone E. Simon-Lledó et al. 10.1016/j.pocean.2018.11.003
30. An Interpretable Multi-Model Machine Learning Approach for Spatial Mapping of Deep-Sea Polymetallic Nodule Occurrences I. Gazis et al. 10.1007/s11053-024-10393-7
31. Multi-scale variations in invertebrate and fish megafauna in the mid-eastern Clarion Clipperton Zone E. Simon-Lledó et al. 10.1016/j.pocean.2020.102405
32. An online path planning algorithm for autonomous marine geomorphological surveys based on AUV Y. Zhang et al. 10.1016/j.engappai.2022.105548
33. Quantitative mapping and predictive modeling of Mn nodules' distribution from hydroacoustic and optical AUV data linked by random forests machine learning I. Gazis et al. 10.5194/bg-15-7347-2018
34. Deep-ocean polymetallic nodules as a resource for critical materials J. Hein et al. 10.1038/s43017-020-0027-0
35. Adaptive distributed observer-based predictive formation tracking control for autonomous underwater vehicles B. Xu et al. 10.1016/j.oceaneng.2023.113886
36. Assessing plume impacts caused by polymetallic nodule mining vehicles P. Weaver et al. 10.1016/j.marpol.2022.105011
37. Digging deep: lessons learned from meiofaunal responses to a disturbance experiment in the Clarion-Clipperton Zone N. Lefaible et al. 10.1007/s12526-023-01353-0
38. Environment, ecology, and potential effectiveness of an area protected from deep-sea mining (Clarion Clipperton Zone, abyssal Pacific) D. Jones et al. 10.1016/j.pocean.2021.102653
39. The megafauna community from an abyssal area of interest for mining of polymetallic nodules B. De Smet et al. 10.1016/j.dsr.2021.103530
40. Using multibeam backscatter strength to analyze the distribution of manganese nodules: A case study of seamounts in the Western Pacific Ocean M. Wang et al. 10.1016/j.apacoust.2020.107729
41. AUV Navigation Correction Based on Automated Multibeam Tile Matching J. Mohrmann & J. Greinert 10.3390/s22030954
42. Implementation of an automated workflow for image-based seafloor classification with examples from manganese-nodule covered seabed areas in the Central Pacific Ocean B. Mbani et al. 10.1038/s41598-022-19070-2
43. Analysis-ready optical underwater images of Manganese-nodule covered seafloor of the Clarion-Clipperton Zone B. Mbani & J. Greinert 10.1038/s41597-023-02245-5
44. Megafauna community assessment of polymetallic-nodule fields with cameras: platform and methodology comparison T. Schoening et al. 10.5194/bg-17-3115-2020
45. Continuous fixed-time formation control for AUVs using only position measurements Y. Wang et al. 10.1016/j.oceaneng.2022.113476
46. Conflicting Narratives of Deep Sea Mining A. Hallgren & A. Hansson 10.3390/su13095261
47. Developing best environmental practice for polymetallic nodule mining - a review of scientific recommendations S. Christiansen & S. Bräger 10.3389/fmars.2023.1243252
48. Automated estimation of offshore polymetallic nodule abundance based on seafloor imagery using deep learning A. Tomczak et al. 10.1016/j.scitotenv.2024.177225
49. Comparing deep-sea polymetallic nodule mining technologies and evaluating their probable impacts on deep-sea pollution L. Sitlhou & P. Chakraborty 10.1016/j.marpolbul.2024.116762
50. Numerical Simulation of Deep-Sea Sediment Transport Induced by a Dredge Experiment in the Northeastern Pacific Ocean K. Purkiani et al. 10.3389/fmars.2021.719463
51. Diversity, distribution and composition of abyssal benthic Isopoda in a region proposed for deep-seafloor mining of polymetallic nodules: a synthesis S. Kaiser et al. 10.1007/s12526-023-01335-2
52. Attitude Stabilization Control of Autonomous Underwater Vehicle Based on Decoupling Algorithm and PSO-ADRC X. Wu et al. 10.3389/fbioe.2022.843020
53. Large-scale bedrock outcrop mapping on the NE Atlantic Irish continental margin A. Recouvreur et al. 10.3389/fmars.2023.1258070
54. Deep learning–assisted biodiversity assessment in deep-sea benthic megafauna communities: a case study in the context of polymetallic nodule mining D. Cuvelier et al. 10.3389/fmars.2024.1366078
55. Scars in the abyss: reconstructing sequence, location and temporal change of the 78 plough tracks of the 1989 DISCOL deep-sea disturbance experiment in the Peru Basin F. Gausepohl et al. 10.5194/bg-17-1463-2020
56. Importance of Spatial Autocorrelation in Machine Learning Modeling of Polymetallic Nodules, Model Uncertainty and Transferability at Local Scale I. Gazis & J. Greinert 10.3390/min11111172
57. Deep Seabed Mining: A Note on Some Potentials and Risks to the Sustainable Mineral Extraction from the Oceans W. Leal Filho et al. 10.3390/jmse9050521
58. Undertaking Deep-Sea Mining: A Quest for the Right Answers R. Sharma 10.4031/MTSJ.56.4.13