A Comparison of Three Biomass Estimation Methods: A Case Study of <i>Pinus tabulaeformis</i> Forests in China
Bibliographic record
Abstract
Power model, linear model and hyperbolic model were commonly used to estimate forest biomass via stand volume, however the relative accuracy is unclear forPinus tabulaeformisforests in China. In order to compare the accuracies of these models, data from 130Pinus tabulaeformisforest stands were compiled from published literatures. Data of 100 stands were randomly selected to establish regression equations, the other 30 data were used to compare the accuracies of equations either established in this study or in previous studies. The results show that biomass ofPinus tabulaeformisforests could be well estimated by power model and linear model, while hyperbolic model is likely to result in enormous overestimation or underestimation. The mean relative errors of the power model and linear model established in this study are-0.3% and 1.8% respectively. In comparison with models established by previous studies, these two models have better prediction accuracies.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".