How to improve allometric equations to estimate forest biomass stocks? Some hints from a central African forest
Bibliographic record
Abstract
Predicting the biomass of a forest stand using forest inventory data and allometric equations involves a chain of propagation of errors going from the sampling error to the tree measurement error. Using a biomass data set of 101 trees in a tropical rain forest in Gabon, we compared two sources of error: the error due to the choice of allometric equation, assessed using Bayesian model averaging, and the biomass measurement error when tree biomass is calculated from tree volume rather than directly weighed. Differences between allometric equations resulted in a between-equation error of about 0.245 for log-transformed biomass compared with a residual within-equation error of 0.297. Because the residual error is leveled off when randomly accumulating trees whereas the between-equation error is incompressible, the latter turned out to be a major source of error at the scale of a 1 ha plot. Measuring volumes rather than masses resulted in an error of 0.241 for log-transformed biomass and an average overestimation of the biomass by 19%. These results confirmed the choice of the allometric equation as a major source of error but unexpectedly showed that measuring volumes could seriously bias biomass estimates.
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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 | 0.016 | 0.066 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".