Nicholas A. S. Hamm
Ecological Modeling · 2011–2018
A researcher in the Science corpus: 3 papers; trusts 23 researchers and is trusted by 0 researchers here, as inferred from the citation graph.
Subfields
- Ecological Modeling — 55 citations into it
- Nature and Landscape Conservation — 47 citations into it
- Ecology — 36 citations into it
- Artificial Intelligence — 12 citations into it
- Ecology, Evolution, Behavior and Systematics — 8 citations into it
- Statistics and Probability — 7 citations into it
- Genetics — 6 citations into it
Whom Nicholas A. S. Hamm trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Jerome H. Friedman
- Jane Elith
- Miguel B. Araújo
- Vladimir Naumovich Vapni
- C. Meynard
- Catherine H. Graham
- Trevor J. Hastie
- Michael P. Austin
- Antonio Gasparrini
- Antoine Guisan
Papers from Nicholas A. S. Hamm's perspective
Ordered by how strongly the researchers Nicholas A. S. Hamm trusts endorse each paper. Same corpus, a different order for every researcher.
- Predictive habitat distribution models in ecology
- Greedy function approximation: A gradient boosting machine.
- A Coefficient of Agreement for Nominal Scales
- New developments in museum-based informatics and applications in biodiversity analysis. (Trends in Ecology & Evolution)
- An evaluation of methods for modelling species distributions
- Do they? How do they? WHY do they differ? On finding reasons for differing performances of species distribution models
- Mapping Species Distributions: Spatial Inference and Prediction
- Ensemble forecasting of species distributions. (Trends in Ecology & Evolution)
- The Nature of Statistical Learning Theory
- Effects of species’ ecology on the accuracy of distribution models
Their papers
- A machine learning method to estimate PM2.5 concentrations across China with remote sensing, meteorological and land use information. (2018) — 515 citations
- Where is positional uncertainty a problem for species distribution modelling (2014) — 1,676 citations
- Spatial autocorrelation in predictors reduces the impact of positional uncertainty in occurrence data on species distribution modelling (2011) — 129 citations