Sebastian H. M. Hickman

Comment on egusphere-2026-74

Xingpei Ye, Lin Zhang, Xiaolin Wang, Ni Lu et autres

Abstract. Understanding how meteorology influences surface ozone variability is critical for interpreting trends and designing effective air quality policies. This study employs explainable machine learning (XML) with SHapley Additive exPlanations (SHAP) to interpret daily ozone variations from 2013 to 2023 across three …

cn, gb (code pays fourni par la source)

Deciphering the impacts of meteorology on surface ozone variability in eastern China using explainable machine learning models

Xingpei Ye, Lin Zhang, Xiaolin Wang, Ni Lu et autres

Abstract. Understanding how meteorology influences surface ozone variability is critical for interpreting trends and designing effective air quality policies. This study employs explainable machine learning (XML) with SHapley Additive exPlanations (SHAP) to interpret daily ozone variations from 2013 to 2023 across three …

cn, gb (code pays fourni par la source)

Supplementary material to "Deciphering the impacts of meteorology on surface ozone variability in eastern China using explainable machine learning models"

Xingpei Ye, Lin Zhang, Xiaolin Wang, Ni Lu et autres

gb, us (code pays fourni par la source)

Applications of Machine Learning and Artificial Intelligence in Tropospheric Ozone Research

Sebastian H. M. Hickman, Makoto Kelp, Paul Thomas Griffiths, Kelsey Doerksen et autres

Machine learning (ML) is transforming atmospheric chemistry, offering powerful tools to address challenges in tropospheric ozone research, a critical area for climate resilience and public health. As in adjacent fields, ML approaches complement existing research by learning patterns from ever-increasing volumes of …

gb, us, nl, de, cn (code pays fourni par la source)

Correction: Directed acyclic graphs in perioperative observational research–A systematic review and critique against best practice recommendations

Matthew Lamont Watson, Sebastian H. M. Hickman, Kaya Marlen Dreesbeimdiek, Katharina Köhler et autres

[This corrects the article DOI: 10.1371/journal.pone.0281259.].

Comment on egusphere-2024-3739

Sebastian H. M. Hickman, Makoto Kelp, Paul T. Griffiths, Kelsey Doerksen et autres

Abstract. Machine learning (ML) is transforming atmospheric chemistry, offering powerful tools to address challenges in tropospheric ozone research, a critical area for climate resilience and public health. As in adjacent fields, ML approaches complement existing research by learning patterns from ever-increasing volumes …

gb, us, nl, de, cn (code pays fourni par la source)

Causal climate emulation

Julien Boussard, Sebastian H. M. Hickman, Ilija Trajkovic, Julia Kaltenborn et autres

Making projections of possible future climates with models is essential to improve our understanding of the causes and implications of anthropogenic climate change. While Earth system models are currently the most complete description of the Earth system, these models are computationally expensive. …

ca, gb, de, il (code pays fourni par la source)

Applications of Machine Learning and Artificial Intelligence in Tropospheric Ozone Research

Sebastian H. M. Hickman, Makoto Kelp, Paul Thomas Griffiths, Kelsey Doerksen et autres

Abstract. Machine learning (ML) is transforming atmospheric chemistry, offering powerful tools to address challenges in tropospheric ozone research, a critical area for climate resilience and public health. As in adjacent fields, ML approaches complement existing research by learning patterns from ever-increasing volumes …

gb, us, nl, de, cn (code pays fourni par la source)

Applications of Machine Learning and Artificial Intelligence in Tropospheric Ozone Research

Sebastian H. M. Hickman, Makoto Kelp, Paul T. Griffiths, Kelsey Doerksen et autres

Machine learning (ML) is transforming atmospheric chemistry, offering powerful tools to address challenges in tropospheric ozone research, a critical area for climate resilience and public health. As in adjacent fields, ML approaches complement existing research by learning patterns from ever-increasing volumes of …

de (code pays fourni par la source)

Estimating the causal effect of temperature on ozone air pollution

Sebastian H. M. Hickman, Paul Thomas Griffiths, Peer Johannes Nowack, Alexander Thomas Archibald

Ground level ozone is an air pollutant which contributes to hundreds of thousands of premature deaths annually. Ground level ozone concentrations are controlled by physical and chemical processes, which can be sensitive to meteorological variables such as the local temperature. Understanding how …

gb, de (code pays fourni par la source)

Development of Machine‐Learning Algorithms to Predict Attainment of Minimal Clinically Important Difference After Hip Arthroscopy for Femoroacetabular Impingement Yield Fair Performance and Limited Clinical Utility

Matthew H. Pettit, Sebastian H. M. Hickman, Ajay Malviya, Vikas Khanduja

PURPOSE: To determine whether machine learning (ML) techniques developed using registry data could predict which patients will achieve minimum clinically important difference (MCID) on the International Hip Outcome Tool 12 (iHOT-12) patient-reported outcome measures (PROMs) after arthroscopic management of femoroacetabular impingement syndrome …

gb (code pays fourni par la source)

Accurate delineation of individual tree crowns in tropical forests from aerial RGB imagery using Mask R‐CNN

James Ball, Sebastian H. M. Hickman, Toby Jackson, Xian Jing Koay et autres

Abstract Tropical forests are a major component of the global carbon cycle and home to two‐thirds of terrestrial species. Upper‐canopy trees store the majority of forest carbon and can be vulnerable to drought events and storms. Monitoring their growth and mortality is …

fr, gb, ca, gf (code pays fourni par la source)