| A machine learning approach to geochemical mapping C Kirkwood, M Cave, D Beamish, S Grebby, A Ferreira Journal of Geochemical Exploration 167, 49-61, 2016 | 119 | 2016 |
| Stream sediment geochemistry as a tool for enhancing geological understanding: An overview of new data from south west England C Kirkwood, P Everett, A Ferreira, B Lister Journal of Geochemical Exploration 163, 28-40, 2016 | 68 | 2016 |
| Towards implementing artificial intelligence post-processing in weather and climate: proposed actions from the Oxford 2019 workshop SE Haupt, W Chapman, SV Adams, C Kirkwood, JS Hosking, ... Philosophical Transactions of the Royal Society A 379 (2194), 20200091, 2021 | 61 | 2021 |
| A framework for probabilistic weather forecast post-processing across models and lead times using machine learning C Kirkwood, T Economou, H Odbert, N Pugeault Philosophical Transactions of the Royal Society A 379 (2194), 20200099, 2021 | 31 | 2021 |
| Indoor radon measurements in south west England explained by topsoil and stream sediment geochemistry, airborne gamma-ray spectroscopy and geology A Ferreira, Z Daraktchieva, D Beamish, C Kirkwood, TR Lister, M Cave, ... Journal of environmental radioactivity 181, 152-171, 2018 | 25 | 2018 |
| Bayesian deep learning for spatial interpolation in the presence of auxiliary information C Kirkwood, T Economou, N Pugeault, H Odbert Mathematical Geosciences 54 (3), 507-531, 2022 | 24 | 2022 |
| Bayesian deep learning for mapping via auxiliary information: a new era for geostatistics? C Kirkwood, T Economou, N Pugeault arXiv preprint arXiv:2008.07320, 2020 | 4 | 2020 |
| Deep covariate-learning: optimising information extraction from terrain texture for geostatistical modelling applications C Kirkwood arXiv preprint arXiv:2005.11194, 2020 | 4 | 2020 |
| Unmixing and mapping components of Northern Ireland’s geochemical composition using FastICA and random forests C Kirkwood, M Cooper, A Ferreira, D Beamish EarthArXiv, 2020 | 3 | 2020 |
| Factsheets of Methods for Raw Materials Intelligence. H2020-Project MICA, Deliverable D4. 1: 182 p E van der Voet, R Shaw, E Petavratzi, L van Oers, C Kirkwood, C Fleming, ... | 3 | 2016 |
| Geological mapping in the age of artificial intelligence C Kirkwood, doi.org/10.1144/geosci2022-023 Geoscientist 32 (3), 16-23, 2022 | 2 | 2022 |
| Environmental factors influencing pipe failures AM Tye, C Kirkwood, R Dearden, BG Rawlins, RM Lark, RL Lawley, ... British Geological Survey, 2017 | 2 | 2017 |
| A dropout-regularised neural network for mapping arsenic enrichment in SW England using MXNet C Kirkwood British Geological Survey, 2016 | 2 | 2016 |
| Uncovering individualised treatment effect: Evidence from educational trials ZM Xiao, O Hauser, C Kirkwood, DZ Li, B Jones, S Higgins OSF Preprints, 2020 | 1 | 2020 |
| Can learning regression features by computer vision improve the generalisation of geostastistical interpolators? C Kirkwood, T Economou, H Odbert, N Pugeault EGU General Assembly Conference Abstracts, EGU-6656, 2023 | | 2023 |
| Methods in machine learning for probabilistic modelling of environment, with applications in meteorology and geology C Kirkwood University of Exeter, 2023 | | 2023 |
| A deep mixture density network for outlier-corrected interpolation of crowd-sourced weather data C Kirkwood, T Economou, H Odbert, N Pugeault arXiv preprint arXiv:2201.10544, 2022 | | 2022 |
| Bayesian deep learning for large scale environmental data modelling C Kirkwood, T Economou, H Odbert, N Pugeault Alan Turing Institute, 2021 | | 2021 |
| Data from: Towards Implementing AI Post-processing in Weather and Climate: Proposed Actions from the Oxford 2019 Workshop WE Chapman, SE Haupt, C Kirkwood, S Lerch, M Matsueda, ... | | 2020 |
| User guide for the British Geological Survey Stream Sediment Geochemistry (500m grid) dataset C Kirkwood, R Lister, F Fordyce, R Lawley British Geological Survey, 2017 | | 2017 |
Nicolas PugeaultReader, School of Computing Science, University of GlasgowVerified email at glasgow.ac.uk
