Converting Abandoned Agricultural Lands to Intensive Hybrid Poplar Plantations: Effects on Soil Organic Carbon Stocks
Bibliographic record
Abstract
ABSTRACT Intensively managed fast‐growing plantations can provide a significant portion of the world's wood biomass and preserve natural or extensively managed forest ecosystems by limiting harvesting pressure and associated disturbances. However, the establishment of plantations should not be at the expense of soil organic carbon stocks, and their potential as a carbon source or sink may depend on their initial stocks prior to planting. The choice of plantation sites is therefore crucial in minimizing losses or allowing the accumulation of carbon from a hybrid poplar plantation. The aim of our study was to determine the impact of afforestation with fast‐growing hybrid poplars on soil carbon stocks of sites of different origins: Abandoned agricultural land (AAL) with a herbaceous vegetation cover; shrubby AAL; and logged (previously forested) sites. Our results showed that 15 years post‐afforestation, previously forested sites where fast‐growing hybrid poplar plantations were established had lower soil organic carbon stocks than their non‐afforested equivalents and than other plantations established on AALs, while plantations on AALs had similar soil organic carbon stocks to their non‐afforested counterparts. AALs would therefore appear to be the preferred establishment site for taking advantage of the high yield of hybrid poplars while preserving soil carbon stocks.
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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 | 0.001 | 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".