Carbon Binding of Different Provenances of Douglas fir (Pseudotsuga menziesii Mirb. Franco)
Bibliographic record
Abstract
Forest tree species are very important in terms of carbon sequestration. Trees store 50% to 75% of the carbon they take up from the atmosphere in their wood, and the rest is released back into the atmosphere during respiration. Douglas fir is forest tree species native to North America, but shown successful growth and high productivity and quality in Europe. Douglas-fir is also considered as a species with high carbon sequestration, with the yearly average carbon storage of 46,46 kg CO2/year and for this reason it is increasingly popular as a tree for planting. This research aims to select the best provenances of Douglas fir for carbon sequestration in the provenance test in Bosnia and Herzegovina. Material for this research were Douglas fir trees in provenance test in Bosnia and Herzegovina, locality Batalovo brdo near Sarajevo. The provenance test was established in 1966. by planting 2+2-year-old seedlings, and included 5 provenances from Washington, Oregon and Canada, and from altitudes of 150-900 m above the sea. Heights and diameter at breast height of 52-year-old trees were measured, and volumes of trees were calculated. The results showed that the lowest average volume had provenance from the altitude of 900 m, 83-3.0 (0.7313 m3), and the highest provenance from 300 m, 65-1,0 (1.3410 m3). If there are 625 trees per ha, provenance 83-3,0 would produce 457 m3/ha, and provenance 65-1,0 838 m3/ha, which indicates differences in carbon sequestration. The obtained results can be used in selection of provenance for using in introduction of Douglas fir in Bosnia and Herzegovina.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.000 | 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".