Allometric equations for predicting aboveground biomass of beech-hornbeam standsin the Hyrcanian forests of Iran
Bibliographic record
Abstract
A better understanding of the carbon biomass from forests is needed to improve both models and mitigation efforts related to the global C cycle and greenhouse gas mitigation. Despite the importance of Hyrcanian forests for biodiversity conservation, no study with biomass destruction has been done to predict biomass and carbon pools from this forest. Mixed-specific regression equations with 45 sample trees using different input variables such as diameter, height and wood density were developed to estimate the aboveground biomass of beech-hornbeam stands. All the sample trees were harvested and the diameter at breast height (DBH) spanned from 31 to 104 cm so as to represent the diameter distribution reported in the beech-hornbeam stand management. Using only diameter as an input variable, the stands regression model estimates the aboveground biomass of the stand with an average deviation of 19% (R2adj = 0.92; SEE = 0.22). Adding height as the second explanatory variable slightly improved the estimation with an average deviation of 18% (R2adj = 0.95; SEE = 0.17). Adding only height or wood density did not improve significantly the estimations. Using the three variables together improved the precision of bole biomass prediction of stands with an average deviation of 10.3% (R2adj = 0.965; SEE = 0.167). 68% of the observed variation in the aboveground biomass of beech-hornbeam stands was explained only by diameter.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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 teacher head, 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".