Additivity of nonlinear biomass equations
Why is this work in the frame?
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
No Canadian affiliation. An affiliation-only frame — the usual design — would never have seen this work. It is one of the works that make the case for inverting the frame.
Machine scores (provisional)
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
- Teacher spread
- 0.264 · how far apart the two teachers sit on this one work
- Validation status
score_only:v0-immature-baseline· verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it
Abstract
Two procedures that guarantee the property of additivity among the components of tree biomass and total tree biomass utilizing nonlinear functions are developed. Procedure 1 is a simple combination approach, and procedure 2 is based on nonlinear joint-generalized regression (nonlinear seemingly unrelated regressions) with parameter restrictions. Statistical theory is given for construction of confidence and prediction intervals for the two procedures. Specific examples using slash pine (Pinus elliottii Engelm. var. elliottii) biomass data are presented to demonstrate and clarify the methods behind nonlinear estimation, additivity, error modeling, and the formation of confidence and prediction intervals. Theoretical considerations and empirical evidence indicate procedure 2 is generally superior to procedure 1. It is argued that modeling the error structure is preferable to using the logarithmic transformation to deal with the problem of heteroscedasticity. The techniques given are applicable to any quantity that can be disaggregated into logical components.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
The record
- Venue
- Canadian Journal of Forest Research
- Topic
- Forest ecology and management
- Field
- Environmental Science
- Canadian institutions
- —
- Funders
- U.S. Forest Service
- Keywords
- HeteroscedasticityAdditive functionMathematicsNonlinear systemNonlinear regressionSeemingly unrelated regressionsApplied mathematicsBiomass (ecology)Tree (set theory)StatisticsLogarithmEconometricsRegression analysisEcologyMathematical analysis
- Has abstract in OpenAlex
- yes