Additional biomass estimation alternatives: nonlinear two- and three-stage least squares and full information maximum likelihood for slash pine
Bibliographic record
Abstract
A system of nonlinear biomass component equations was developed for slash pine (Pinus elliottii Engelm. var. elliottii) trees using an econometric approach in which endogenous right-hand-side variables were included in some equations. The system was fitted to component biomass data from 306 slash pine trees sampled in the southeastern United States with weighted two-stage (2SLS) and three-stage (3SLS) least squares and full information maximum-likelihood (FIML) estimation methods. The predictive performances of the system fitted with these three estimation methods were ranked based on an array of statistics, and the ranking follows the order of FIML > 3SLS > 2SLS. The new system performed as well or better than previously published biomass equation systems developed using the aggregation and disaggregation approaches and fitted to the same data. The results demonstrated that the econometric approaches such as FIML and 3SLS have the potential to be useful for tree biomass modeling.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
Machine scores (provisional)
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.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".