FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W2021869106> ?p ?o ?g. }

• W2021869106 endingPage "381" @default.
• W2021869106 startingPage "375" @default.
• W2021869106 abstract "Allometric relationships are commonly used to estimate average biomass of trees of a particular size and to predict biomass of individual trees based on an easily measured covariate variable such as stem diameter. They are typically power relationships which, for the purpose of data fitting, are transformed using natural logarithms to convert the model to its linear equivalent. Implementation of these equations to estimate the relationships and to predict biomass of new trees on the natural (i.e., actual) scale requires back-transforming the logarithmic predictions. Because these transformations involve non-linearity, care must be taken during this step to avoid bias. Several correction factors have been proposed in the literature for removing the gross bias in estimates, but their performance as predictors of biomass has not yet been examined. This is a very important problem, and here we review nine such correction factors in terms of their abilities to estimate biomass and predict biomass for new trees. We compare their performance by examining their bias and variability based on large datasets of above-ground biomass and stem diameter for eight species of harvested trees and shrubs in the genera Eucalyptus and Acacia (n = 102–365 individuals per species). We found that good estimates of average biomass turned out to be good predictors of biomass for new trees. The linear model fitted has log of the above-ground biomass as the response variable and log of the stem diameter as the covariate. The only exactly unbiased estimate among those considered was the uniform minimum variance unbiased (UMVU) estimate, which involves evaluating a confluent hypergeometric function to obtain its correction factor. Three alternative correction factors that are easy to compute also performed well. One of these minimises mean squared error and was found to result in low bias, low prediction bias, the lowest mean squared error, and the lowest mean squared prediction error among all correction factors examined." @default.
• W2021869106 created "2016-06-24" @default.
• W2021869106 creator A5015058588 @default.
• W2021869106 creator A5025099112 @default.
• W2021869106 creator A5034272045 @default.
• W2021869106 creator A5034620438 @default.
• W2021869106 creator A5073724497 @default.
• W2021869106 date "2013-12-01" @default.
• W2021869106 modified "2023-10-08" @default.
• W2021869106 title "Correction factors for unbiased, efficient estimation and prediction of biomass from log–log allometric models" @default.
• W2021869106 cites W1592616442 @default.
• W2021869106 cites W1767254696 @default.
• W2021869106 cites W1828937196 @default.
• W2021869106 cites W1965465712 @default.
• W2021869106 cites W1970010064 @default.
• W2021869106 cites W1973483819 @default.
• W2021869106 cites W1982585616 @default.
• W2021869106 cites W1994515379 @default.
• W2021869106 cites W1996995980 @default.
• W2021869106 cites W1998742066 @default.
• W2021869106 cites W2000084758 @default.
• W2021869106 cites W2001341908 @default.
• W2021869106 cites W2022074394 @default.
• W2021869106 cites W2025098293 @default.
• W2021869106 cites W2030006368 @default.
• W2021869106 cites W2056581871 @default.
• W2021869106 cites W2057989179 @default.
• W2021869106 cites W2066894778 @default.
• W2021869106 cites W2083847822 @default.
• W2021869106 cites W2090137677 @default.
• W2021869106 cites W2098935748 @default.
• W2021869106 cites W2107173766 @default.
• W2021869106 cites W2110287480 @default.
• W2021869106 cites W2141559248 @default.
• W2021869106 cites W2799720164 @default.
• W2021869106 cites W4237405726 @default.
• W2021869106 doi "https://doi.org/10.1016/j.foreco.2013.08.041" @default.
• W2021869106 hasPublicationYear "2013" @default.
• W2021869106 type Work @default.
• W2021869106 sameAs 2021869106 @default.
• W2021869106 citedByCount "51" @default.
• W2021869106 countsByYear W20218691062014 @default.
• W2021869106 countsByYear W20218691062015 @default.
• W2021869106 countsByYear W20218691062016 @default.
• W2021869106 countsByYear W20218691062017 @default.
• W2021869106 countsByYear W20218691062018 @default.
• W2021869106 countsByYear W20218691062019 @default.
• W2021869106 countsByYear W20218691062020 @default.
• W2021869106 countsByYear W20218691062021 @default.
• W2021869106 countsByYear W20218691062022 @default.
• W2021869106 countsByYear W20218691062023 @default.
• W2021869106 crossrefType "journal-article" @default.
• W2021869106 hasAuthorship W2021869106A5015058588 @default.
• W2021869106 hasAuthorship W2021869106A5025099112 @default.
• W2021869106 hasAuthorship W2021869106A5034272045 @default.
• W2021869106 hasAuthorship W2021869106A5034620438 @default.
• W2021869106 hasAuthorship W2021869106A5073724497 @default.
• W2021869106 hasBestOaLocation W20218691062 @default.
• W2021869106 hasConcept C105795698 @default.
• W2021869106 hasConcept C115540264 @default.
• W2021869106 hasConcept C119043178 @default.
• W2021869106 hasConcept C121955636 @default.
• W2021869106 hasConcept C134306372 @default.
• W2021869106 hasConcept C144133560 @default.
• W2021869106 hasConcept C153026981 @default.
• W2021869106 hasConcept C163175372 @default.
• W2021869106 hasConcept C18903297 @default.
• W2021869106 hasConcept C196083921 @default.
• W2021869106 hasConcept C33923547 @default.
• W2021869106 hasConcept C34153902 @default.
• W2021869106 hasConcept C39927690 @default.
• W2021869106 hasConcept C42060753 @default.
• W2021869106 hasConcept C86803240 @default.
• W2021869106 hasConceptScore W2021869106C105795698 @default.
• W2021869106 hasConceptScore W2021869106C115540264 @default.
• W2021869106 hasConceptScore W2021869106C119043178 @default.
• W2021869106 hasConceptScore W2021869106C121955636 @default.
• W2021869106 hasConceptScore W2021869106C134306372 @default.
• W2021869106 hasConceptScore W2021869106C144133560 @default.
• W2021869106 hasConceptScore W2021869106C153026981 @default.
• W2021869106 hasConceptScore W2021869106C163175372 @default.
• W2021869106 hasConceptScore W2021869106C18903297 @default.
• W2021869106 hasConceptScore W2021869106C196083921 @default.
• W2021869106 hasConceptScore W2021869106C33923547 @default.
• W2021869106 hasConceptScore W2021869106C34153902 @default.
• W2021869106 hasConceptScore W2021869106C39927690 @default.
• W2021869106 hasConceptScore W2021869106C42060753 @default.
• W2021869106 hasConceptScore W2021869106C86803240 @default.
• W2021869106 hasLocation W20218691061 @default.
• W2021869106 hasLocation W20218691062 @default.
• W2021869106 hasOpenAccess W2021869106 @default.
• W2021869106 hasPrimaryLocation W20218691061 @default.
• W2021869106 hasRelatedWork W1995238475 @default.
• W2021869106 hasRelatedWork W2063510451 @default.
• W2021869106 hasRelatedWork W2262124246 @default.
• W2021869106 hasRelatedWork W2346177637 @default.
• W2021869106 hasRelatedWork W2461476137 @default.
• W2021869106 hasRelatedWork W2518633982 @default.

• next