Biomass Estimation Errors Associated with the Use of Published Regression Equations of Paper Birch and Trembling Aspen
Bibliographic record
Abstract
Abstract Since forest biomass contributes a significant proportion of global carbon cycle, obtaining accurate estimate of forest biomass is important. The root mean squared error (RMSE), the percents of the mean observed values were used to compare the precision of local and published biomass equations for paper birch and trembling aspen. With the exception of stemwood biomass equations, the biomass equations for these two species tended to be stand specific. Measured as percent of mean observed values, the values of biomass/tree predicted from the published equations for paper birch varied from 49.9% to 140.2% for foliage and from 155% to 238.7% for live branches; the estimates for trembling aspen ranged from 71.8% to 81.3% for foliage and from 55.3% to 164.5% for live branches. There were large discrepancies between the measured data and the published equations in graphical form as well as biomass estimates, particularly for foliage, live branches, and stembark. Clearly, published regression equations should be checked for their applicability before they are used to estimate the biomass of particular stands.
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.
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 | 0.023 | 0.079 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".