The following are just a selection of my publications. For a complete listing, please visit my ORCID.
A Bolt, C Houston, JJ Dabrowski, PM Kuhnert (2021) An emulation framework for fire front spread, Machine Learning and the Physical Sciences, NeurIPS 2021. [https://arxiv.org/abs/2203.12160]
D MacKinlay, D Pagendam, T Cui, PM Kuhnert, D Robertson, S Janardhanan (2021) Model inversion for spatio-temporal processes using the fourier neural operator, Machine Learning and the Physical Sciences, NeurIPS 2021.[https://ml4physicalsciences.github.io/2021/files/NeurIPS_ML4PS_2021_8.pdf]
M Wellington, P Kuhnert, L Renzullo and R Lawes (2022) Modelling within-season variation in light use efficiency enhances productivity estimates for cropland, Remote Sensing, 14, DOI: 10.3390, [https://www.mdpi.com/2072-4292/14/6/1495#]
M Wellington, P Kuhnert, and R Lawes, (2023) Rapid monitoring of cropland primary productivity and shipping activity in Ukraine, PLOS one, 18, DOI: e0286637, [https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0286637]
M Wellington, P Kuhnert, R Lawes, L Renzullo, J Pittock et al. (2023) Decoupling crop production from water consumption at some irrigation schemes in southern Africa, Agricultural Water Management, 284, 30, 108358, [https://www.sciencedirect.com/science/article/pii/S0378377423002238#:~:text=In%20summary%2C%20decoupling%20of%20crop,result%20in%20increased%20water%20demands.]
C Sanderson, E Schleiger, D Douglas, P Kuhnert and Q Lu (2024) Resolving ethics trade-offs in implementing responsible AI, 2024 IEEE Conference on Artificial Intelligence (CAI), 25-27 June, Singapore, [10.1109/CAI59869.2024.00215]https://ieeecai.org/2024/wp-content/pdfs/540900b205/540900b205.pdf
Lydia R. Lucchesi, Petra M. Kuhnert, Jenny L. Davis, Lexing Xie. 2022. Smallset Timelines: A Visual Representation of Data Preprocessing Decisions, In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22), June 21–24, 2022, Seoul, Republic of Korea. ACM, New York, NY, USA, 23 pages, [https://dl.acm.org/doi/abs/10.1145/3531146.3533175]
Nelson, Sam, Lucchesi, Lydia, Petra Kuhnert (2022) VizumApp: A Shiny App for visualizing uncertainty in spatial data using the Vizumap R package, DOI: 102.100.100/439688. [https://data.csiro.au/collection/csiro:54870]
Link to the VizumApp Shiny App is here
LR Lucchesi, PM Kuhnert, CK Wikle (2021) Vizumap: an R package for visualising uncertainty in spatial data, Journal of Open Source Software 6 (59), 2409. [https://joss.theoj.org/papers/10.21105/joss.02409]
M Wellington, R Lawes, and P Kuhnert (2023) A framework for modelling spatio-temporal trends in crop production using generalized additive models, Computers and Electronics in Agriculture, 212, 108111.
MC Quigley, LG Bennetts, P Durance, PM Kuhnert et al. (2019) The provision and utility of earth science to decision-makers: synthesis and key findings, Environment Systems and Decisions 39 (3), 349-367. [https://strathprints.strath.ac.uk/69617/1/Quigley_etal_ESD_2019_The_provision_and_utility_of_earth_science_to_decision_makers.pdf]
MC Quigley, LG Bennetts, P Durance, PM Kuhnert et al. (2019) The provision and utility of science and uncertainty to decision-makers: earth science case studies, Environment Systems and Decisions 39 (3), 307-348. [https://link.springer.com/article/10.1007/s10669-019-09728-0]
PM Kuhnert et al. (2018) Making management decisions in the face of uncertainty: a case study using the Burdekin catchment in the Great Barrier Reef, Marine and Freshwater Research. [https://doi.org/10.1071/MF17237]
DW Gladish, PM Kuhnert et al. (2016) Spatio-temporal assimilation of modelled catchment loads with monitoring data in the Great Barrier Reef, The Annals of Applied Statistics 10 (3), 1590-1618. [https://www.jstor.org/stable/43956894?seq=1#metadata_info_tab_contents]
Kuhnert et al. (2012) Quantifying total suspended sediment export from the Burdekin River catchment using the loads regression estimator tool, Water Resources Research, 48. [https://agupubs.onlinelibrary.wiley.com/doi/full/10.1029/2011WR011080] [LRE R Package: https://github.com/pkuhnert/LRE]
Kuhnert, P.M. (2014) Physical-statistical modelling, Environmetrics, 202-201. [https://onlinelibrary.wiley.com/doi/10.1002/env.2276]
Kuhnert et al. (2012) Predicting fish diet composition using a bagged classification tree approach: a case study using yellowfin tuna (Thunnus albacares), Marine biology 159 (1), 87-100. [https://link.springer.com/article/10.1007/s00227-011-1792-6]