S. Wills
Environmental Engineering · with Boettinger · 2015–2019
A researcher in the Science corpus: 2 papers; trusts 15 researchers and is trusted by 2 researchers here, as inferred from the citation graph.
Subfields
- Environmental Engineering — 24 citations into it; #4,162 of 5,479 by trust here, #3,356 by citations
- Artificial Intelligence — 15 citations into it; #29,267 of 35,345 by trust here, #24,417 by citations
- Analytical Chemistry — 13 citations into it
- Soil Science — 8 citations into it; #1,447 of 2,166 by trust here, #1,537 by citations
- Statistics and Probability — 8 citations into it
Whom S. Wills trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Max Kuhn
- Budiman Minasny
- David Ruppert
- Hervé Abdi
- Bradley Efron
- Tomislav Hengl
- R. V. Rossel
- Marvin N. Wright
- Alex B. McBratney
- Harald Martens
Who trusts S. Wills overall
Papers from S. Wills's perspective
Ordered by how strongly the researchers S. Wills trusts endorse each paper. Same corpus, a different order for every researcher.
- The Elements of Statistical Learning: Data Mining, Inference, and Prediction
- Methods to interpolate soil categorical variables from profile observations: Lessons from Iran
- Extrapolating regional soil landscapes from an existing soil map: Sampling intensity, validation procedures, and integration of spatial context
- Classification and Regression by randomForest
- Estimation and Accuracy after Model Selection
- Confidence Intervals for Random Forests: The Jackknife and the Infinitesimal Jackknife
- Using data mining to model and interpret soil diffuse reflectance spectra.
- A conditioned Latin hypercube method for sampling in the presence of ancillary information (Computational Geosciences)
- Using model averaging to combine soil property rasters from legacy soil maps and from point data
- Validation requirements for diffuse reflectance soil characterization models with a case study of VNIR soil C prediction in Montana
Their papers
- Accurate and Precise Prediction of Soil Properties from a Large Mid-Infrared Spectral Library (2019) — 139 citations
- Machine learning for predicting soil classes in three semi-arid landscapes (2015) — 345 citations