Effect of measurement errors on the estimation of tree biomass
Bibliographic record
Abstract
Diameter at breast height (DBH) is commonly used to predict the aboveground biomass (AGB) of forests and to derive biomass models for single trees; however, there is evidence that measurement errors of DBH have not been previously considered. In this study, two types of measurement errors were evaluated: errors in national forest inventory data (NFID) and errors in a calibration data set (CDS). Using Monte Carlo simulations, the uncertainties arising from these two measurement errors were quantified. In addition, the effects of measurement errors on estimates under different error assumptions were analyzed to determine how these two uncertainties change with increasing errors. The results show that CDS measurement error contributes more to the total uncertainty, whereas NFID measurement error has a negligible effect on estimating the biomass of regional forests. The uncertainties of both types of measurement error increased with increasing error assumptions; however, the uncertainties caused by CDS measurement error were noticeably larger than those caused by NFID measurement error. Thus, the greatest potential for reducing uncertainties caused by measurement error lies in increasing the accuracy of DBH measurements in CDS.
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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 | 0.019 | 0.099 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".