Rachael Griffiths

ORCID: 0000-0003-1381-0718
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About
Contact & Profiles
Research Areas
  • Fish Ecology and Management Studies
  • Hydrological Forecasting Using AI
  • Bat Biology and Ecology Studies
  • Climate Change and Environmental Impact
  • Coastal and Marine Management
  • Face recognition and analysis
  • Species Distribution and Climate Change
  • Water Quality and Pollution Assessment
  • Hydrology and Watershed Management Studies
  • Education Practices and Evaluation
  • Image Processing and 3D Reconstruction
  • Handwritten Text Recognition Techniques
  • Environmental and Air Quality Management

University of Cambridge
2024

Swansea University
2019

Australian National University
2013

Abstract The paradigm‐changing opportunities of biologging sensors for ecological research, especially movement ecology, are vast, but the crucial questions how best to match most appropriate and sensor combinations specific biological analyse complex data, mostly ignored. Here, we fill this gap by reviewing optimize use techniques answer in ecology synthesize into an Integrated Biologging Framework (IBF). We highlight that multisensor approaches a new frontier biologging, while identifying...

10.1111/1365-2656.13094 article EN Journal of Animal Ecology 2019-08-19

The management of freshwater ecosystems is usually targeted through the regulation water quantity (limiting diversions and providing environmental flows) quality (setting limits or targets for constituent concentrations). Climate change likely to affect in multiple ways future requires predictions plausible conditions. We use a suite ecologically-relevant hydrological indicators determine significance projected climate-driven changes Upper Murrumbidgee River Catchment south eastern Australia...

10.1007/s00477-013-0744-8 article EN cc-by Stochastic Environmental Research and Risk Assessment 2013-06-10

This poster presents our AI-enhanced workflow to digitise a vast corpus of manuscripts, hand-written in an extremely difficult form Tibetan. These offer unique glimpse into archaic non-Buddhist rituals and narratives, enabling us reconstruct Tibetan Pagan religion for the first time. The palm-leaf page layout differs from any previously trained models wide range abbreviations, mixture scripts languages, varying image quality make task challenging. To tackle these challenges, we developed...

10.33774/coe-2024-6d77x preprint EN cc-by-nc-nd 2024-11-14
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