Dr Carlo Mattone ~ Postdoctoral Research Fellow
College of Science & Engineering
- About
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- Research Disciplines
- Socio-Economic Objectives
- Publications
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These are the most recent publications associated with this author. To see a detailed profile of all publications stored at JCU, visit ResearchOnline@JCU. Hover over Altmetrics badges to see social impact.
- Journal Articles
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- Mattone C and Sheaves M (2017) Patterns, drivers and implications of dissolved oxygen dynamics in tropical mangrove forests. Estuarine Coastal and Shelf Science, 197. pp. 205-213
- Sheaves J, Dingle L and Mattone C (2016) Biotic hotspots in mangrove-dominated estuaries: macro-invertebrate aggregation in unvegetated lower intertidal flats. Marine Ecology Progress Series, 556. pp. 31-43
- Davis B, Mattone C and Sheaves M (2014) Bottom-up control regulates patterns of fish connectivity and assemblage structure in coastal wetlands. Marine Ecology Progress Series, 500. pp. 175-186
- Current Funding
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Current and recent Research Funding to JCU is shown by funding source and project.
MAKO Tidal Turbines Pty Ltd - Contract Research
Barney Point Turbine Monitoring
- Indicative Funding
- $27,457
- Summary
- Monitoring surface video, underwater video, underwater acoustic and sidescan data streams using AI (January-April 2019) (including monthly regular reporting, final reporting and feasibility analysis), to assess whether the tidal turbine impacts fish and other aquatic organisms during its operations.
- Investigators
- Marcus Sheaves, Carlo Mattone and Dmitry Konovalov (College of Science & Engineering)
- Keywords
- Artificial Intelligence; Impact Assessment; Tidal turbine monitoring
World Wide Fund for Nature, Australia - Consultancy
Queensland Coral Reef Fin Fish Fishery Electronic Observer
- Indicative Funding
- $6,000
- Summary
- To understand the value of a new technique for collecting critical fisheries information regarding target, bycatch and threatened species interactions in the coral reef fin fish fishery through electronic observation and machine learning.
- Investigators
- Marcus Sheaves and Carlo Mattone (College of Science & Engineering)
- Keywords
- Artificial Intelligence; Fisheries Management; Line Fishers
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