Regression models of carbon and CO<sub>2</sub> sequestration of hybrid poplar plantations in northern Serbia
Bibliographic record
Abstract
This research contributes to the assessment of the potential for carbon dioxide (CO2) sequestration in poplar plantations, through applying regression models of total and annual sequestration as related to stand age. The study was carried out in northern Serbia, in plantations of Populus ×euramericana (Dode) Guinier clone I-214. Based on the measured data obtained during the field research, a modeling framework for quantifying carbon (C) sequestration in forest ecosystems (CO2FIX version 3.1) was used to calculate the total carbon stored in fresh (aboveground and belowground) biomass, in soil organic matter, and in wood products. Four regression models were created as a part of the research: (i) total C sequestration model, (ii) total CO2 sequestration and carbon credit (CO2e) generation model, (iii) annual C sequestration model, and (iv) annual CO2 sequestration and annual carbon credit generation model. The stand-level research results indicated that the total sequestration of C for a 30-year production cycle was 78.58 t C·ha−1, while the average value for all years of a 30-year production cycle was 44.02 t C·ha−1. The average annual sequestration of C for all years of a 30-year production cycle was 2.62 t C·ha−1·year−1, while the average annual sequestration of CO2 or the average annual changes in CO2 stocks for all years of a 30-year production cycle was 9.60 t CO2·ha−1·year−1.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".