| Evaluation of metal partitioning and mobility in a sulfidic mine tailing pile under oxic and anoxic conditions PX Pinto, SR Al-Abed, C Holder, DJ Reisman Journal of Environmental Management 140, 135-144, 2014 | 12 | 2014 |
| Abundant and persistent sulfur‐oxidizing microbial populations are responsive to hypoxia in the Chesapeake Bay K Arora‐Williams, C Holder, M Secor, H Ellis, M Xia, A Gnanadesikan, ... Environmental microbiology 24 (5), 2315-2332, 2022 | 10 | 2022 |
| Can machine learning extract the mechanisms controlling phytoplankton growth from large-scale observations?–A proof-of-concept study C Holder, A Gnanadesikan Biogeosciences 18 (6), 1941-1970, 2021 | 10 | 2021 |
| Using neural network ensembles to separate ocean biogeochemical and physical drivers of phytoplankton biogeography in Earth system models C Holder, A Gnanadesikan, M Aude-Pradal Geoscientific Model Development 15 (4), 1595-1617, 2022 | 3 | 2022 |
| Major trends and environmental correlates of spatiotemporal shifts in the distribution of genes compared to a biogeochemical model simulation in the Chesapeake Bay S Preheim, S Morris, Y Zhang, C Holder, K Arora-Williams, P Gensbigler, ... bioRxiv, 2023.01. 09.523340, 2023 | 1 | 2023 |
| Dataset and scripts for manuscript" Using Neural Network Ensembles to Separate Ocean Biogeochemical and Physical Drivers of Phytoplankton Biogeography in Earth System Models" C Holder, A Gnanadesikan, M Aude-Pradal Johns Hopkins Univ., Baltimore, MD (United States), 2021 | 1 | 2021 |
| How well do Earth System Models capture apparent relationships between phytoplankton biomass and environmental variables? C Holder, A Gnanadesikan Global Biogeochemical Cycles 37 (7), e2023GB007701, 2023 | | 2023 |
| Random Forest-based Understanding of Earth System Model Predictions of Phytoplankton Diatom S Dutta, A Gnanadesikan, C Holder AGU Fall Meeting Abstracts 2022, OS32B-1023, 2022 | | 2022 |
| Earth System Models Capture the General Trends of Phytoplankton Detected in Observations C Holder, A Gnanadesikan Authorea Preprints, 2022 | | 2022 |
| USING MACHINE LEARNING TO UNDERSTAND PHYTOPLANKTON PHYSIOLOGY IN NATURAL ENVIRONMENTS AND EARTH SYSTEM MODELS C Holder Johns Hopkins University, 2021 | | 2021 |
| Using Neural Network Ensembles to Separate Biogeochemical and Physical Components in Earth System Models C Holder, A Gnanadesikan, M Aude-Pradal Geoscientific Model Development Discussions 2021, 1-34, 2021 | | 2021 |
| Using Machine Learning to Find Relationships in Oceanographic Datasets C Holder, A Gnanadesikan Ocean Sciences Meeting 2020, 2020 | | 2020 |
| Visualizing Variable Interactions in a Biogeochemical Model using Random Forests and Neural Networks C Holder, A Gnanadesikan AGU Fall Meeting Abstracts 2018, OS21C-1587, 2018 | | 2018 |
| Uncovering the Drivers of Chlorophyll Variability in the North Atlantic using Random Forests C Holder, A Gnanadesikan 2018 Ocean Sciences Meeting, 2018 | | 2018 |
| Assessing the Impact of Removing Select Materials from Coal Mine Overburden, Central Appalachia Region, USA PX Pinto, SR Al-Abed, CD Holder, R Warner, J McKernan, S Fulton, ... Mine water and the environment 37 (1), 31, 2018 | | 2018 |
| Metal release from mine tailings under oxic and anoxic conditions SR Al-Abed, PX Pinto, CD Holder ABSTRACTS OF PAPERS OF THE AMERICAN CHEMICAL SOCIETY 246, 2013 | | 2013 |
| Using random forests to compare the sensitivity of observed particulate inorganic and particulate organic carbon to environmental conditions R Jin, A Gnanadesikan, CD Holder | | |
| Comparing Biogeochemical Model Outputs using Neural Network Ensembles C Holder, A Gnanadesikan | | |
| Random Forests and a Potential Function for the Chesapeake Bay C Holder, A Gnanadesikan | | |
Anand GnanadesikanJohns Hopkins UniversityVerified email at jhu.edu
