A comparison of fast-growing maritime pine (<i>Pinus pinaster</i> Aiton.) plantations with native broadleaved vegetation for greenhouse gas balances
Bibliographic record
Abstract
Establishing fast-growing plantations is a long-term strategic climate change mitigation option, since these plantations may absorb carbon at an accelerated rate and alleviate the pressure on natural forests. In Istanbul, nearly 5% of the forests, totaling 32 603 ha of natural oak–beech forest ecosystems, were converted to maritime pine ( Pinus pinaster Aiton.) plantations in the 1990s. Maritime pine grows faster than native mixed broadleaf forests but introduces a higher fire risk. The objective of this study was to assess the greenhouse gas (GHG) consequences of these conversions by analyzing wildfire emissions and carbon stock changes for a period of two decades after conversion. The carbon modeling was done using the Carbon Budget Model of the Canadian Forest Sector model calibrated with ground measurements. The results revealed that the total ecosystem carbon stocks would remain at 97.9 tC/ha (Avoided Species Conversion scenario) compared to 116.7 tC/ha in the Business as Usual (BAU) scenario. The BAU scenario refers to real-life conditions that the species conversions have occurred. The fire emissions had a minor share in total ecosystem GHG balance, because the burnt area rate was low (around 0.1%) during the assessment period.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".