Aboveground biomass and leaf area equations for three common tree species of Hyrcanian temperate forests in northern Iran
Bibliographic record
Abstract
Biomass equations are essential for evaluating the climate change mitigation potential of forests through biomass accumulation and carbon sequestration. In northern Iran’s Hyrcanian forests, topographic relief is so extreme that developing biomass equations from destructive sampling of trees is physically challenging. In this paper, allometric biomass and leaf area equations were developed for three common Hyrcanian tree species: Oriental beech (Fagus orientalis Lipsky), chestnut-leaved oak (Quercus castaneifolia C.A.Mey.), and common hornbeam (Carpinus betulus L.). A total of 30 trees, ranging in diameter at breast height (DBH) from 21 to 90 cm, were felled and stems, branches, twigs, and leaves from each tree were measured and weighed. Allometric equations for estimating biomass from DBH and height and their combinations were derived. Model comparison and selection were based on R2, Akaike’s information criterion (AIC), prediction error sums of squares, model standard error estimate (SEE), ΔAIC, and correction factor. The best-fit equations had adjusted R2 values between 0.81 to 0.98 and SEE values between 0.351 and 0.681. The allometric equations provide improved methods for predicting forest biomass and carbon storage in Hyrcanian forests from standard forestry measurements, which means these equations may be applied to historical and new forest data.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".