Nilsun Göçer

Nature and Landscape Conservation · 2018

A researcher in the Science corpus: 1 paper; trusts 12 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

Whom Nilsun Göçer trusts

Their strongest outbound trust, strongest first, inferred from their citations.

  1. Klaus von Gadow (in Nature and Landscape Conservation)
  2. Mahadev Sharma (in Nature and Landscape Conservation)
  3. Hailemariam Temesgen (in Nature and Landscape Conservation)
  4. S. Huang (in Environmental Engineering)
  5. G. Montero (in Nature and Landscape Conservation)
  6. Rafael Calama (in Nature and Landscape Conservation)
  7. J. Parton (in Nature and Landscape Conservation)
  8. Q. V. Cao (in Nature and Landscape Conservation)
  9. Som Bohora (in Nature and Landscape Conservation)
  10. F. crecente-campo (in Nature and Landscape Conservation)

Papers from Nilsun Göçer's perspective

Ordered by how strongly the researchers Nilsun Göçer trusts endorse each paper. Same corpus, a different order for every researcher.

  1. Interregional nonlinear height-diameter model with random coefficients for stone pine in Spain
  2. Comparative evaluation of five height–diameter models developed for black spruce and jack pine stand-types in terms of goodness-of-fit, lack-of-fit and predictive ability
  3. A generalized height–diameter model including random components for radiata pine plantations in northwestern Spain
  4. Developing and Validating Nonlinear Height-Diameter Models for Major Tree Species of Ontario's Boreal Forests
  5. Height–diameter equations for boreal tree species in Ontario using a mixed-effects modeling approach
  6. A comparison of alternative methods for estimating the self-thinning boundary line
  7. Regional mixed-effects height–diameter models for loblolly pine (Pinus taeda L.) plantations (European Journal of Forest Research)
  8. Prediction of tree diameter growth using quantile regression and mixed-effects models
  9. Improved Calibration of Nonlinear Mixed-Effects Models Demonstrated on a Height Growth Function (Forestry sciences)
  10. Estimating Crimean juniper tree height using nonlinear regression and artificial neural network models

Their papers